diff --git a/diffphys-code-ns.ipynb b/diffphys-code-ns.ipynb
index 68cc645..f46226a 100644
--- a/diffphys-code-ns.ipynb
+++ b/diffphys-code-ns.ipynb
@@ -84,13 +84,7 @@
     "id": "da1uZcDXdVcF",
     "outputId": "66973d88-ec2c-4137-ed5b-1696848be36f"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": []
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install --upgrade --quiet phiflow==2.2\n",
     "from phi.torch.flow import *  \n",
@@ -126,9 +120,7 @@
       ]
      },
      "execution_count": 2,
-     "metadata": {
-      "tags": []
-     },
+     "metadata": {},
      "output_type": "execute_result"
     }
    ],
@@ -180,14 +172,13 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 720x432 with 4 Axes>"
       ]
      },
      "metadata": {
-      "needs_background": "light",
-      "tags": []
+      "needs_background": "light"
      },
      "output_type": "display_data"
     }
@@ -262,7 +253,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Velocity dimensions: (inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᵛ=2)\n"
+      "Velocity dimensions: (inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᶜ=x,y)\n"
      ]
     }
    ],
@@ -304,12 +295,20 @@
     "outputId": "93dae39b-1b31-429a-cf81-f615e23bbf7d"
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/thuerey/miniconda3/envs/tf/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3427: SyntaxWarning: Specifying wrt by position is deprecated in phi.math.funcitonal_gradient() and phi.math.jacobian(). Please pass a list or comma-separated string of parameter names.\n",
+      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Some gradient info: StaggeredGrid[(inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᵛ=2), size=(32, 40), extrapolation=0]\n",
-      "(xˢ=31, yˢ=40) float32  -17.366662979125977 < ... < 14.014090538024902\n"
+      "Some gradient info: StaggeredGrid[(inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᶜ=x,y), size=\u001b[94m(x=32, y=40)\u001b[0m \u001b[93mint64\u001b[0m, extrapolation=\u001b[94m0\u001b[0m]\n",
+      "\u001b[92m(xˢ=31, yˢ=40)\u001b[0m \u001b[94m2.61e-08 ± 8.5e-01\u001b[0m \u001b[37m(-2e+01...1e+01)\u001b[0m\n"
      ]
     }
    ],
@@ -334,11 +333,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "id": "2LTHHjtZ_tro"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 864x360 with 5 Axes>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# neat phiflow helper function:\n",
     "vis.plot(field.vec_length(velocity_grad)) # show magnitude"
@@ -387,16 +409,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization step 0, loss: 1193.145020\n",
-      "Optimization step 1, loss: 1165.816650\n",
-      "Optimization step 2, loss: 1104.294556\n",
-      "Optimization step 10, loss: 861.661743\n",
-      "Optimization step 20, loss: 775.154846\n",
-      "Optimization step 30, loss: 747.199829\n",
-      "Optimization step 40, loss: 684.146729\n",
-      "Optimization step 50, loss: 703.087158\n",
-      "Optimization step 60, loss: 660.258423\n",
-      "Optimization step 70, loss: 649.957214\n"
+      "Optimization step 0, loss: 298.286163\n",
+      "Optimization step 1, loss: 291.454376\n",
+      "Optimization step 2, loss: 276.057831\n",
+      "Optimization step 9, loss: 233.706482\n",
+      "Optimization step 19, loss: 232.652145\n",
+      "Optimization step 29, loss: 178.186951\n",
+      "Optimization step 39, loss: 176.523254\n",
+      "Optimization step 49, loss: 169.360931\n",
+      "Optimization step 59, loss: 167.578674\n",
+      "Optimization step 69, loss: 175.005310\n",
+      "Optimization step 79, loss: 169.943680\n"
      ]
     }
    ],
@@ -435,14 +458,13 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x288 with 6 Axes>"
       ]
      },
      "metadata": {
-      "needs_background": "light",
-      "tags": []
+      "needs_background": "light"
      },
      "output_type": "display_data"
     }
@@ -470,14 +492,13 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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/cyNZXwvgFjPbm35/C4BzAXypqDEDdY4NzYgfrhNF46hiCUEsjcjHZBRhmKMMFojTGzR1iycO+836MFpaFQWosjBPlTHGRWKFa0HvOt1AcnvH/FYz29oxfyKAxzrmdwJ4eeC2feue2KuBnejJsYifFiQPCBEF0qoQ9aeYTneb2ZYKrKkEOceiHeiCK0QzkFaFqD/l6/RxACd3zJ+ULgtd9wxn3W/0Y4xGqxDxM588oAx4IeqNtCpE/alGp3cA2ETyVJITAM4HsC3QopsBvIbkWpJrAbwmXVaYrs4xyaUk/5nkD0jeS/JD6fJrSP6Y5F3ptLkfQ4SolMgvuNKpiAZpVVoV9adknZrZDIB3InFq7wdwvZndS/IKkm8AAJIvI7kTwJsB/BXJe9N19wL4z0gc7DsAXDGfnFeUkLCKSQBnmtkzJMcBfJvkP6XfvcfMbujHgGFiJQ0+Hoob1O9LTizyKL/MvShrIPWyBnYvixZkwMer0wrTS4tuOyQpzdVAiLaqTL5rQvIhIK32olU3J4r1Ou22nqqS7Ur7O/dhXhU6NbObANzkLHt/x+c7kIRM+Na9GsDVZdnS1Tk2MwPwTDo7nk7Rn71ERLQgyUc6FVEgrQpRf1qg06AHlSRHSd4FYBeSseRuT7/6MMm7SV5FcklVRgrRN5G/qgWkUxEJ0qq0KupP5DoNco7NbNbMNiN5nH06yV8A8F4ALwTwMgDrAFzmW5fkJSS3k9z+9MzhcqwWohdakuRTlk73TUunYkhIq4tqNavTI4M0WYif0gKd9hTiamb7AdwG4Fwze8ISJgF8Fkl1E986W81si5ltWTW2vG+Dw21l1ymEuRKnIv37oDP5GKEVmsqiaP+V2WfW29Rg+tXp2vHqdGpgz1MdqZuNIb9klZR6HpFWF9RqVqfLPNtr71QWpOWmEEZguSmsv96nWhC5TkNGqziW5Jr08zIkda9/RHJjuowA3gjgnurMFKI4BsDmepuahnQqYkBalVZF/WmDTkNGq9gI4FqSo0ic6evN7B9Ifp3ksUgeXt4F4NLqzBSiDwyNfK3TI9KpaD7SqrQq6k8LdBoyWsXdAF7iWX5mJRYJUQWRC1k6FdEgrQpRfyLXqcpHi1bQxNc6QrQRaVWI+hO7ThvpHFdZvCPk7134hskxe7TgZlxCEmDK/MUad7/YgldAdaAOiWmdlPUnr7IwR9FtF0mwKzPhtjIq0CrJcwF8HMkp99NmdqXz/asBfAzALwI4v8kFc9qML+fLTV7zFacqoouixT3KSqYbupZbcE1tpHMsRM9EfpcrRDSUqNU0rvcTSJLedgK4g+Q2M7uvo9m/ArgQwB+V17MQkRP5NVXOsYgfszaUpBWi+ZSv1dMB7DCzhwGA5HUAzgPwrHNsZo+k30V+uReiJFpwTZVzLNqBLntCNIPetbqB5PaO+a1mtjX9fCKAxzq+2wng5cWNE0IAiP6aWjvneNjxxLl1Am+OQuIt55ygqBHfOk4skW+rbrzRoCM9i/Q39HvMoRsQF8OOLy7roUXI+WbOc/BUGYfsEhJfPOwYxFILE/S+K7vNbEuJFoia4YsVdvFpwI1DHnQBjZD+Bqnd0IImQUR+Ta2dcyxE6RiifwUkRBSUr9XHAZzcMX9SukwIUZQWXFPlHIt2EPkrICGioVyt3gFgE8lTkTjF5wN4e6k9CNFGIr+mdi0fLUQMxF7qUohYKFOrZjYD4J0AbgZwP5JqdPeSvILkGwCA5MtI7gTwZgB/RfLeavdQiOYT+zVVT45F/BgqucvV+KlClEwFWjWzmwDc5Cx7f8fnO5CEWwghQqjomlonhu4cV5mAVwQ3jCY08chdz5dAMJZ7Tu9L9LFF54F8QlyVAf0hiRAhhGzFtxdl9G4o/8415vFTDcNPuOukaGhbWecW33bcJL0RT+ZNkUIdoesU0fygk5GKUIVWY6as83MMlFXgw0dI0Y8qk+9KTaQrgTbodOjOsRCVU81drsZPFaJsWvBESojG0wKdyjkWrcBXWrQLi42dCmj8VCEqoYBWhRADJnadyjkWraDAKyCNnSrEEIj9da0QMVCFTgPyeJYA+ByAlwLYA+CtZvYIyVOQJNw+kDb9rpld2o8tXZ1jkksBfBPAkrT9DWb2gXRonOsArAdwJ4B3mNlUt+0NKsY45O9WNJZxZi6/D+6mvNt21hsd7R5P7Pu1xkaqjDHOzvtipKqKc6vsyKjmFVCtxk8tW6fDpIguQ88rYeeF7LZ8GhgN6q0cvHkHNQs1LS3voQWva2PS6qAomgPhnktGvVqqLn4/RBdlxRMPtBBQBToNzOO5CMA+M3seyfMBfATAW9PvHjKzzWXZEzKU2ySAM83sxQA2AziX5CtSo64ys+cB2JcaLUQtqWDYmWfHTyU5gWT81G1V7kMXpFMRBbEPEQVpVURABTp9No8nvSmcz+Pp5DwA16afbwBwFlnNo4KuzrElPJPOjqeTATgzNQ5IjH1jFQYKUQZmvU3dt1ev8VOlUxELZWu1bkirIgYK6HQDye0d0yXOJn15PCcu1Ca9Bh9A8qYFAE4l+X2S/53kq/rdv6CY4/Rx950AnofksfdDAPanxgH+nZhf9xIAlwDAsROr+rVXiN4x5EJaStlszcZPLUunxy1ZWb2xQvioSKt1o6hWO3V6vHQqhkUxnVaZx/MEgJ8xsz0kXwrg70m+yMyeLrrBoAp5ZjabxnKchOTR9wtDOzCzrWa2xcy2rBpbXsxKIfpgfkzGyF/VlqbTNePSqRgO0mrX9Z7V6drxZVWaKMSCVKTTkDyeZ9uQHAOwGsAeM5s0sz0AYGZ3IrnZfH4/+9jTaBVmtp/kbQB+GcAakmPpne5Qk5HKxA389yUH+d7kzVr39fxrZnGD6n3Jd6MBw+aGJM0VTXJwbdRA9PWiFToNOOZ8KnGP1RAN+LSce6oQkAwz4unKLfpRNPluoMk4ojTaoNVuFNVgUOJsgUtTSMEP73olJftVqeW6FRNxeDaPB8mxfz6AtztttgG4AMB3ALwJwNfNzEgeC2Cvmc2S/FkAmwA83I8xXZ8ckzyW5Jr08zIkmYT3A7gtNQ6psTf2Y4gQ1UGY9TY1DelUxIG0mjaTVkWNKV+nIXk8AD4DYD3JHQDeDeDydPmrAdxN8i4kcfuXmtnefvYw5MnxRgDXpjFSI6nB/0DyPgDXkfwTAN9PjRaiflhzX7/2gHQqmo+0Kq2K+lORTgPyeI4iSXB31/tbAH9bpi1dnWMzuxvASzzLH0YSKyVE7Yn9giudiliQVoWoP7HrVBXyuuCLp3XjiwFgxrq3cY8lX0zLuBNjPO6JL3bjhryvLArGBbtxkUULpdQJQ1iMqkgg8rGwLkXj1asiJL4YCMspcNcb8wh1zvl9fEVBQmIH3Ta++OI2xRNLq+EYmDueu+l22ISeN1xdhmjZd80dca6f7nwori6LxhcX1XLdYoXboFM5xyJ+DLAWDA8lROORVoWoPy3QqZxj0QqaWCxAiDYirQpRf2LXqZxj0QpifwUkRCxIq0LUn9h1KudYtILYXwEJEQvSqhD1J3adyjnugu/NgS+JZ3rOTQ7wrOhku416guzdBLyxke4JBLO+rty7Ok9fvgSGEMoq+jGotzIdtd1FSfgSf+qWpOfD1a43cdZd5pEgR9zEvu4FCHzFe0ZH3IS8wR6oZT39KctuabU/BqnBkOS/suzxbWc2wDkbD6gBXLToRwj5hNvhHty+QkRFaINO5RyLFtDMYgFCtA9pVYj6E79O5RyLVjAX+SsgIWJBWhWi/sSuUznHIn5a8ApIiCiQVoWoPy3QqZxjET1tGLBciBiQVoWoP23QaTTOcUjdm5BqbyFtfMl2086BMuU1yKl+50m2WzqWTa/zJfHMWjbLwJc16k0IDCCkMlERhn2TGbuQRR5vEo9zHEzP5TN2XMX5jt0xZ9u+RBc34dZNvkvalFODda5g4lNQtc0BUwcbRHfKSrYLqVLpTYJ3roO+BMEiFV5DqlSOBiTKJ9vKrldWQlwdiF2n0TjHQixGWU6+EKJapFUh6k/sOpVzLOLHGP2YjEJEgbQqRP1pgU67jgJI8mSSt5G8j+S9JH8/Xf5Bko+TvCudXl+9uUL0ThIf1dvUNKRTEQOxa1U6FTEQu06BsCfHMwD+0My+R3IlgDtJ3pJ+d5WZ/dfqzKsnvvi+GScE6ehsvs24s2jpaL58x7Kx6a59zc1kl/kHY3djtnzFDjyrFaAJx33sr4BQA526x2Edi4LkYo4DdGGeIMQJJ+bQFzs8Ptq9oI8byxh6nJZ1salj3GDkWi1Vp+6xGlNMaye+Yj2Ts9lne76iWkVwNQnkY4x9xTya8NuXWYQkcp12d47N7AkAT6SfD5K8H8CJVRsmRJnU0QkoE+lUxELMWpVORSzErFMgIKyiE5KnAHgJgNvTRe8keTfJq0muLds4IcrAkNzl9jI1GelUNJU2aVU6FU2lDToNdo5JrgDwtwD+wMyeBvBJAD8HYDOSO+E/X2C9S0huJ7n96ZnD/VssRK9Ycpfby9RUytDpvmnpVAyJlmi1DJ3ul07FsGiBToOcY5LjSIT8BTP7OwAws6fMbNbM5gB8CsDpvnXNbKuZbTGzLavGlpdltxA9Mdfj1ETK0unacelUDI/YtVqWTtdIp2KIxK7TrjHHJAngMwDuN7OPdizfmMZPAcCvA7inGhPrh39A8uz8oZl8m5XOr71qYjrXZtlEdsWj091zJn2JT/lCBmF3bk18/dGdZt659kJTdRqSFBryt/Pd5c95iwJkt+Um9QD5AjpLRrsbOTGaP/0vGctq2Zfo49rj3Y/Ij90scWu1DjotKxF7kAloPg1MOQV8fEW1XEIS0nxt3AI+ZSa2hVA/TVSjU5LnAvg4gFEAnzazK53vlwD4HICXAtgD4K1m9kj63XsBXARgFsDvmdnN/dgSMlrFKwG8A8APSd6VLvtjAG8juRlJ+MkjAH6nH0OEqIr5+KjIkU5F42mBVqVT0Xiq0CnJUQCfAHAOgJ0A7iC5zczu62h2EYB9ZvY8kucD+AiAt5I8DcD5AF4E4AQAXyP5fDPLDwkWSMhoFd+GOy5Ywk1FOxVi0NTvzrtcpFMRCzFrVToVsVCBTk8HsMPMHgYAktcBOA9Ap3N8HoAPpp9vAPAX6duY8wBcZ2aTAH5Mcke6ve8UNUYV8kQrKOtVohCiWqRVIepPBTo9EcBjHfM7Abx8oTZmNkPyAID16fLvOuv2NUSinGMHN47KjT8E/EUvpp1Sir6YY3eQ8tXLjubajI9l3wL4Yo7dAdFnPWUcZy0bj+U7kIu+Fmnatcss7qdRVeCLj+2Gezz5itNUVRgkNOHDbTfl0c6ks8w8+zE6kV22dCwveLcIiPdiEnBcxlzww0Va7Y9B3liE9FVlXPIRT75AEdx4Yle3ADDixDOH5A/4KHpsuzHOw9ZIQZ1uILm9Y36rmW0t0axSkXMsWoGv0qAQon5Iq0LUnwI63W1mWxb5/nEAJ3fMn5Qu87XZSXIMwGokiXkh6/ZEObdeQtSc2OvACxELZWuV5LkkHyC5g+Tlnu+XkPxy+v3taXEOIcQiVHBNvQPAJpKnkpxAkmC3zWmzDcAF6ec3Afi6mVm6/PxUy6cC2ATgn/vZPz05FtFjaGaFHiHaRtla7ScDvjQjhIiMKq6paQzxOwHcjGQot6vN7F6SVwDYbmbbkAyD+Pk04W4vEgcaabvrkSTvzQD43X5GqgDkHIuWoFe1QjSDkrVaOAM+fSIlhPBQxTXVzG6CM3KLmb2/4/NRAG9eYN0PA/hwWbbIOS6ALxHBTdw75FYFAbBiLLts5Yp8Qt709Kiz3e7JdjOeNsr4zqLLXG+4iTRhyTfZRiFPFnwJOyF9lVVxadpj45HZ7LIxT/LN0tHsQ4mlnoI+7u8xMzuaa+M+2gh9GlO3m70yE4QKaHWxRJ9+MuB392zJkCmi26LkCth4k9S6b8eftOckoHmO96Nzbv8hxYLyBk04SfBuUjwQlhDn23ZVbyx9RUgGnaQX+zVVzrGInhYUFhAiCgpqtVuijxCiRNpwTZVzLFpBVUOICSHKpWSt9pMBL4RYgNivqXKORfyYwkyEaATla/XZDHgkTvD5AN7utJnPgP8OshnwQggfLbimDtw59sXKdDLswa1D8MX7TTtBkEdm81GR65y4xCUr8oUDju4ez8xPzeTjFN2CIyF3cLG/AlmMNrwCqpqQwfzdk2VIDGLQsevtq5y/59HZ/HYOzmSXLRvNt1m1ZCozv2Qir+UZj3YHSRPOpS5la7WfDPgYKBrTn1+nnGtM0bhkX5tDM9ncm/GAwkVjngIfE+NZ7Y562phzzfXlAg2SYWu7DddUPTkWLYDRvwISIg7K12o/GfBCCB/xX1PlHItWEPsrICFiQVoVov7ErlM5x6IVxH6XK0QsSKtC1J/Yddq1fDTJk0neRvI+kveS/P10+TqSt5B8MP1/bfXmCtE7SXxUb1PTkE5FDEir0qqoP23QaciT4xkAf2hm3yO5EsCdJG8BcCGAW83syrRe/eUALuvXoEEObl30zseXx+wm5LnzAPCcpdmiH6NL8huadpJ4puY8hQOcIiBVHngNPKa9VJE8QPJcAB9HkujzaTO70vl+CYDPAXgpkqGh3mpmj5RuSEJpOk2iybokznq0E1KAwE3IKSuxxWdPt30AgEmPTg869TyOX5pvs2p5Vsvj4/nCAW5Cnu/3cM9tvnNLSMGPYSfolEnsiT4oUashibLd1ilS4CdZr/fEcJ/eR1mspM8hJ5l2uSdx1t3XJeP5xNmJJdllbvIdAMx6lg2SOuo7dp12fXJsZk+Y2ffSzwcB3I+kotB5AK5Nm10L4I0V2ShE31iPUzdIjgL4BIDXATgNwNtInuY0uwjAPjN7HoCrAHyk/z3xI52KWChbq3VDWhUxELtOe4o5JnkKgJcAuB3AcWb2RPrVkwCOW2CdSwBcAgDHTqwqbKgQRTGr5C73dAA7zOxhACB5HZKL230dbc4D8MH08w0A/oIkqx5DtV+dHr9kZZXmCbEgFWm1tvSqVelU1IE26LTrk+N5SK4A8LcA/sDMnu78Lr3Yey/4ZrbVzLaY2ZZVY8v7MlaIosz1OAVwIoDHOuZ3psu8bcxsBsABAOuL7UEYZeh07fiyKk0UYlEq0GotKaLVTp2uGdf1VAyP2HUa9OSY5DgSEX/BzP4uXfwUyY1m9gTJjQB2dd0O8t54yI/mxiEPMv7G19Osx72YdEIOPeFPeM6qQ137OzKVLQLiFvwIJeSuromvOopS4JjZQHJ7x/xWM9taokmlU5ZOgXyMoXs8hcTzjjCkcIBvOyHHbraNP765+3aOeIqAPDOd3djSkfxZ6pgV2SIgszPd4xR9x2DRpy91jEEsi5j3bZ4ytdovZRUK8RG2nfwzupA4ZDc3YNVYfkcmRrMX5mVLp3NtxsayfU1PFSveU9aT1KYc/02xsygho1UQSQWh+83sox1fzZfcRPr/jeWbJ0T/GArd5e6ef0KTTq5j/DiAkzvmT0qXeduQHAOwGkliXulIpyIGCmq1UUiroum0QachYRWvBPAOAGeSvCudXg/gSgDnkHwQwNnpvBBt4Q4Am0ieSnICScnZbU6bzovdmwB8vcJ4Y+lUiGYgrQpRc7qGVZjZt7Hwe86zyjVHiGooe7g7M5sh+U4ANyMZyu1qM7uX5BUAtpvZNiRPhz5PcgeAvUgc6EqQTkUsNHFM1F6QVkUMxK5TVcgTLaCaOvBmdhOAm5xl7+/4fBTAm0vvWIhoqUarQogyiV+nQ3eOQ+I63HgVX6GQESc4vGiMS8jdkG/bB50knmPG8nu2esORzLx5koEmncIBbsEPn42+RIDIb+p6Yr6ajyiOrwhAN0J+c49McgLzJX44clugmEh3Aw7lawLgqJNxu24i32h8eTbRZ/rAeK6Na/fsXH5n3SAbX8GP2BNfOpFWwwkp1hPCIAv6eJNSPfvgS2h3edrJrTt2SX6lZePZRktX5BPyfNdhl1zBk8CfPUS7TdR3G3Q6dOdYiEEQ+12uELEgrQpRf2LXafA4x0I0mdjrwAsRC9KqEPVnkDoluY7kLSQfTP9fu0C7C9I2D5K8oGP5N0g+0JEA+5xufco5FtFjPYpYF1whhoO0KkT9GYJOLwdwq5ltAnBrOp+B5DoAHwDwciQVbD/gONG/YWab06nrGOIDD6voFrvoi58NKRySi0MOiOMJ+YP5mviKgDwznbXq5BX5gcSXrM+ueGRX3sbDM9k/yUxAERBdH7oT+yugsnHrZxQZgC4kTtmnQTcO2TxH+IizaMqjk5BB+Z+eym/btem4ZUdybUbGs43mPHGLM7PZHfHZ48YYh8Yf1q10a5GY9IWQVgdL0YI+rk782+meC+TmCyXbdov85NvscypvzRyTf9a3asXRzPzYsrwF089kr9WVDbaJ4ccXl9n/gHV6HoAz0s/XAvgGgMucNq8FcIuZ7QUAkrcAOBfAl4p0qJhj0Qr0hEmIZiCtClF/BqzT48zsifTzkwCO87Q5EcBjHfM702XzfJbkLJLKlH/SreaAnGMRPYZmVugRom1Iq0LUn4I63UBye8f81s7KsyS/BuB4z3rvy/RtZvQNWbY4v2Fmj5NcicQ5fgeAzy22gpxj0QqG/TpLCBGGtCpE/Smg091mtmXh7dnZC31H8imSG83sCZIbAfhihh/HT0MvAOAkJOEXMLPH0/8PkvwikpjkRZ1jJeSJ6Jm/y425DrwQMSCtClF/hqDTbQDmR5+4AMCNnjY3A3gNybVpIt5rANxMcozkBgAgOQ7g1wDc063DgT85du813GfjQYkdIQNre5aF/IHcgcxD42oOTGcHF3/5svx9x+jqbOD/kYfyP//UnJMcENZ9aYQk+pSZfDMoFMfYH558nFIY9RxLs3Num7xyRx2DZiyfAOsrSuD25xbvAYClTgWCDSsP59q40WrT03kthxQOCHn6UrfkOx9l2iithjOoc7G/CEjv10rvcVJwH/ZOZq+5I1ySa7Ni/VS2Tb5WD2zO1Wn3QjzewltB6+X7rwq3kEvZDFinVwK4nuRFAB4F8BYAILkFwKVmdrGZ7SX5nwHcka5zRbrsGCRO8jiAUQBfA/Cpbh0qrEK0Al1vhWgG0qoQ9WeQOjWzPQDO8izfDuDijvmrAVzttDkE4KW99innWESPoRlP34RoO9KqEPWnDTqVcyxagZ5GCdEMpFUh6k/sOu3qHJO8GkkA8y4z+4V02QcB/EcA/5Y2+2Mzu6mIAUXuPYKKC/jWc3rzFfNw42h8cYu+ZQdtMjP/3OX5mGOOZdd7+tDSXJtJp3BAlTFlRe/8GheX3JJKWlVrtRshf/OQEXjcWMYRT4GPWcvqxBfvd2A6H4e8Yix7Zjg0M5trs35pNjBx9YZ8ERCbcWKeZ/N6n51zioB4znYhWqqyKEFVseSFaYFWq9TpIHMD3GFi5+jRgBt379HAtEffbn++c8t+y+py5dhErs3Ehuz8zMFck1wBHzcGGchrMCS+GMgfy2WNxBJyHvXHiZfSfSt0GjJaxTVIqoy4XNVRiq+Si60QZdCiDPhrIK2KBtMSrV4D6VQ0mDbotOuTYzP7JslTBmCLEJVR5dO3uiCtihiIXavSqYiB2HXazzjH7yR5N8mr0zHlvJC8hOR2ktufnskPiSRE9RBzPU6R0VWrnTrdN50PIxBiMLRaq9KpaAjx67Soc/xJAD8HYDOAJwD8+UINzWyrmW0xsy2rxpYX7E6I/jDrbYqIIK126nTt+LIBmidElpZqVToVjSJ2nRYarcLMnpr/TPJTAP4hZD0yH1hfJCksZI0xbwKBk+jj2ZCbMDDpCc73BaIfZvap+Ckr84k+NpWNvDlwND9oeQOPIS91GualYB34KCii1dJ06tHgSMgR7qzH0XzfE5bVly9haM9U/t5/ytHzrM3k2qx3ZLlkbf7oObI7u21vQp7zm7kJekDYRaMsLfmSmkL6H2TSXlu12s81dTGKJka72vUmkjmL5jwH01xA/0dn84mzuQI6nvWO8Ghmft1EXssja50kvQOTuTaz7jnBo1N3WUjyna9dUS27f0df/0WT9IrQBp0Wco7na1yns7+OgFJ8QgyT2DNrF0JaFU2jjVqVTkXTiF2nIUO5fQnAGQA2kNwJ4AMAziC5GckNxCMAfqc6E4Xon8h1DEBaFXEQu1alUxEDses0ZLSKt3kWf6YCW4SohKSaz7CtqB5pVTSdNmhVOhVNpw06HXqFvCIxUb6onZCtuPFPM56V3GVHZvO9TXmCbSaRjX86fn0+tmnuUHbFwzP5n9+NnSwa7ufumi/WqeixXZ9o4kAamhBQJ4rEIPvii90YSZ/+Q2LnxkeyWlrp0dKTR/Ix/fud+OVxT+GC5y7PxjOPeAr6zBzNxklOe+Im83GKuSY5/DrtHt/oy58guv/NQs6/A9WOtNoXRQrxFMkD8JKXQI6pufzFc9ry2j04lS3Es3Q0v94Sy+r7+KVHc21GjsluZ27WE3M8E5A/4MQlhxbzyMVOFy285cz78we6bzvk3BpEC3Q6dOdYiEEQe/KAELEgrQpRf2LXqZxjET1teAUkRAxIq0LUnzbotJ8iIEI0ButxEkIMh0FqleQ6kreQfDD9f6HiG18luZ9k0BBrQsRO7NdUOceiFcxZb5MQYjgMWKuXA7jVzDYBuDWd9/FnAN7Rd29CRELs19SBh1W4IeMhv5m7TtGBzeEk8cx57g3cIPMD0/kg98P5scZzrDphKrdser9bgMAzkHf3TQ8d99evu83JnWvdraw3IQl4brKHr0CBq11voRBn2eiIp42j5Q0e+x49uDS37JjxrOaXjeXPAf/rxqeyCzyPEI4cyRYXmPIm5LmFDHx6L3Yuy/89fL9jto2vL3c7hc+tJTEErZ6HZFg1ALgWwDcAXOY2MrNbSZ7hLh8mvmI9+Tae4wK96zTIHstHobr9T3sKbPgcp0cPZ12TlWP5Rs8ZWZmZP2ntrvyGlmZ1OnMk3//kVLYvf0JedlnRxLbCRUDc7fjaBCXXlqOtNlxTFXMsWkET71yFaCMD1upxHcU3ngRw3EB7F6KhxH5NlXMsWkHsQhYiFgpodQPJ7R3zW81s6/wMya8BON6z3vs6Z8zMWNpYV0LETezXVDnHInqamhAgRNsoqNXdZrZlwW2anb3QdySfmi/dTHIjAM+7eSFEJ224piohT8RPj4kDsd8RC1FbBq/VbQAuSD9fAODGvrcoROy04JraiCfHbqC5rxpUke3QE9bu5OtgT76gDg57SuuNOPcZEz+TTwY6cGc2k8+thuejaAB/SBKjLyEwqPKZsy3fXtQtVN+iv8+tHyHV70LajI3O5tosWZLV0pq1R3Jtvjf3eG7ZhiMnZObP2XBsrs0pb8jOTz6QP088fThbnWvKk8QTkuwWljSXW5RLhvFffNzfuntCYNEqemUyYK1eCeB6khcBeBTAWwCA5BYAl5rZxen8twC8EMAKkjsBXGRmNw/S0BBCokLKqlLp4latBIBxT7tcm8l8Jcv792e3tXZJXl+bVmeT7Z5zWv4cAKdy5uThvMszOZ1d5ibfAflEfXq0NOrZfzcBzldVbs7Rsq9iYb6NbztZqtbtIHVKch2ALwM4BcAjAN5iZvs87b4K4BUAvm1mv9ax/FQA1wFYD+BOAO8ws/yoCR3oybGIHkP8d7lCxMCgtWpme8zsLDPbZGZnm9nedPn2ecc4nX+VmR1rZsvM7KQ6OsZCDIohXFP7HXLxIwCuMrPnAdgH4KJuHco5Fq3AepyEEMNBWhWi/gxYp+chGWoR6f9v9NpkdiuAg53LmDzmPxPADd3W76Src0zyapK7SN7TsSyoqpAQdaENT46lVREDsWtVOhUxUECnG0hu75gu6aG7foZcXA9gv5nNx+LtBHBit5VCYo6vAfAXAD7XsWz+EfeVJC9P53MDp/twz2VFBqEPGTjfG9/nzI/nx+3HqvFsLONPDuW3c3g2XwVkqS3P9vWzy3NtJv/Hbo+lWdzfI+TcX2Z8by5uydemZoUDQvDFekXINShBq2bFY917xRvjGrDe2Fj2SF31knx046Nf/lZu2UMzezPzZ6x7f96m//jWzPzuX/9irs2eo/mcAhd3P3zFTFzFjRb82X0D8ucdR885sYaFQlqg1WtQ4jV1MXzxqy4hhXhCCCnWMz6Wzx/YMDWRW/Y/pn6UmT/+6Mm5Nv/p1FWZ+YlXn5JrM/vDbN7BoUP5+Ga3gE9IoYxRbxmOYrj9zXm6H8kFPXffbtXn8AI6XXRUmboNudjVOTazb5I8xVl8HgKqCglRBwx5pz9GpFXRdNqgVelUNJ0qdFrhkIt7AKwhOZY+PT4JQD5b26FozLGqColGEfur2kWQVkWjaKlWpVPRKJoy5KKZGYDbALypl/X7TshLO15w10leMh9jcmD6cL/dCdE7lrwC6mWKkcW02qnTfdOeIZGEGATSarhOp6RTMSQGr9MrAZxD8kEAZ6fzILmF5KfnG6VDLv4NgLNI7iT52vSrywC8m+QOJDHIn+nWYdFxjoMfcadlPLcCwPNXbIzwVCbqThte1S5CkFY7dXrayuOkUzEUWqzVnnX6olXSqRgOg9apme0BcJZn+XYAmSEXF1j/YQCn99JnUed4/hH3leizqlC3BD0AGPcmsvSOmzDgG7T8OKfgwI9m9+baHOL+3LJTbVNm3p733Fybw0cyI4x4i3CUdbZzEyqKBuf7BBDyusHdj2EXBYnxCVMgpWg1Pwh+93VCciZ8yS+uLqZm8pmzPJxN4ll9OF+t54QV+dyPnU9/OzN/xdt35Ld96Jcy87sPHpNrM+kk8fgSmHJFfjxiolPRyHdO8iXbzbqb9vzUbrKdv012oa+vkEIhZdJSrZZ2Te1GvqhW/gd3tevTqdvGp3f3mjsxkU9mf+7Y/tyynxy+MzP/0GT+5/j6f3h7Zn72tb+da3N026cz8/sPr861KVL4igUzZ91iHr7+vdfXgOu5u17VOWux67Src0zyS0gSBTaklYE+gAWqCglRV9rwNEpaFTEQu1alUxEDses0ZLSKty3wVe4RtxB1xGCwAd/m9lvusgjSqmg6w9DqoJFORdNpg05VIU+Iaui33KUQQgghhkDRmONClFVcwBcX5+IfOiS7cJT5FwPrjzmamd9l+ZjEp/Z/J7fs9rdk/Zu5016fa7P70EOZ+aOz+VjKQoOvewdxd5d4thvwt/DFms25/fnin3KxjHkGGYc8hCGfzkPAuKVmdivJM9zlw6bbQ4GiDw3c42l6rvv9+axnVPxnJrOD+R/+Vr4IyJaRfOGAV214WWb++998JtfmZT/71cz8+EixZwjueWrG85uNlqQCr05z57v8eq4u8ueNwRPR8GzRUFb86vR0/pp34FC+oM76ZS/IzD8+lS+gNbreKehxpPtoWIem8+cJNy7aVwhnzG3jiykYzRc4Ccu7yM774pLp5A/kioIAuQtqSJx4P8Su04E6x0IMiwI63kBye8f81jRTPBSNWypEASK/5goRBbHrVM6xiB5DobvcRUtdAvUrdylE0ymoVSHEAGmDTuUci/ixaoRcYblLIdpJRVoVQpRIC3SqhDzRCqzHfyVQuNylEG1mCFoVQvRI7Dod+pNjN0HPl1wWtp3svDsAPgBMO4k9c5bf/WXT2WWrR07Mtdk9uiq37PF/yw4uvnZ/btSu3AD/viIgRfBvxR3oPWw9XxB/VYQUCilDUkN6BeQdt5TkFgCXmtnF6fy3ALwQwIp0zNOLzOzmgVvbhdxA9QULfLjbmQ1JyPMtc7T81KHluTanrc0n32xakd3aCWsO5toc+Hq2oMiRmQ25Nq6Wxzy/x0xp+i528Lq/v9+cgL+jo8yi9oTQhte1dcMX8eVeL8r6m/jOCT/Yuya37AX24sz8y9fli5tNPfLjzPzSb38318Z1A6bNc76Zy2p51NNkxknA8yW3hiTAhZwTRzz6yq3n+ZuFnKPLur63QadDd46FGASDHpKx33KXQrSVyIdPFSIKYtepnGPRCtxhrYQQ9URaFaL+xK5TOceiFcR+lytELEirQtSf2HU6cOfY/T3dsbTzQ4TnmfEUBXDxxcPMOFv3xeT8YM/azPyLR9bm2qxfkR+y9nt7sjHHv/Clr+baLJ/IxkDOHcrbGLL/7m/mi9osOri/GyPli4secZblioIAuQDHoBjVAPuKYIi/DnzZdCvWU7SYj3s8+Qp8hBT5CSke8oKV+WjlF615OjO/79CyXJspJ+/AF2MbUuCjSLGior+r7/gOybbO99c9/tS3X2XFIUur9SQkVtZXTnhmNnsUHp3JuxzLx/J/8V9csyIz//yV+TaTe7PbHrv90Vyb/T/J6vuYselcmxDNhWi5LHz2uJdPtygIkC8M4ismUqTImI826FRPjkUriL0OvBCxIK0KUX9i16mcYxE/LRiTUYgokFaFqD8t0KmcYxE9ySugyJUsRARIq0LUnzbotC/nmOQjAA4iGYp0plu5XSGGReRvgLoirYqm0GatSqeiKcSu0zKeHP+qme0Obdwt+N0XMO4+vi/6N3GTgaZn8+lvR50EoZ9ZkW/zsyPrc8tevDZb9GPvN6dybZ6ZPCYzf8xoPmEopHCA+xv61hkLSIjzBueH9O/MhyQRDhODRX+XG0hPWu0kpBCEm7Tj03quMEVJiS6+vtZP5JNvTthwIDP/r7vyCbfuIPzjo/nUk8NHske9r3iRu6xoMozvNwp5pela7eveV8ygCGX9HaVVAH3otCzc48tXrCeXkBdwfLsJegCwdMSXkJe9Nj53+dFcm4P7lmb7fyjfZtJJrvWekypMtgs6Jwb1X44miib8urRBpwqrEK0g9rtcIWJBWhWi/sSu05DRfhbDAPx/JO8keYmvAclLSG4nuf3AzOE+uxOiGHPpnW7oFCGLarVTp/umjwzBPCESWq7VcJ1OSadieAxSpyTXkbyF5IPp//lXfkm7r5LcT/IfnOXXkPwxybvSaXO3Pvt1jn/FzH4JwOsA/C7JV7sNzGyrmW0xsy2rx5b32Z0QvZPUgbeepghZVKudOl07nh/7V4hBIK32oNMJ6VQMhyHo9HIAt5rZJgC3pvM+/gzAOxb47j1mtjmd7urWYV9hFWb2ePr/LpJfAXA6gG/2s80QCg+Un4vVzbcZczZ90jH5RktH8ssOTWcLfOzauzLXxi1c4Cuw4dpYPE5RdGIt/0V60aqBXePg/HGwblGAgsY6jHliEkeZXTZn+VOZr1DI8tVOHPKufH8HpyYy8zOe7fhijF2K7H7R+GIf/T75GBZt1mq/19RcjKtHxqMFfl9//kB2np7r4qijXZ87v3Qqn3uzbiLb37HL8k/Jj05lNT9yYInHxux2DnmKkLhaHvecb0KLaLmEnBNDCna56/mKsrhJBb6+PLVDCjNgnZ4H4Iz087UAvgHgMreRmd1K8gx3eREKnz9JHkNy5fxnAK8BcE8ZRglRNnM9TjEhrYom0VatSqeiSRTQ6Yb5kKB08obiLsBxZvZE+vlJAPkyxd35MMm7SV5FMn8n5dDPk+PjAHwlLWM4BuCLZpavmSzEkGnDmIxdkFZFI2i5VqVT0QgK6nT3YkMTkvwagOM9X70v07eZkT2/Un8vEqd6AsBWJE+dr1hshcLOsZk9DODFRdcXYnBY9KUuF0NaFc2hvVqVTkVzKF+nZnb2Qt+RfIrkRjN7guRGeAPiFt32/FPnSZKfBfBH3dZpaliaED3R8gx4IRqDtCpE/RmwTrcBuCD9fAGAG3tZOXWoweS1zBsREK400HGOk0fxvVPWwNVuctu459bAHAvXjOfbuEl7ADBt2Y1NzuRLY0w5iT1HPYk+Re5WfMH57m827Kxu39+waLJhr7T8VW0pFEkamQsY3N5NrAPySSPjI/mEHXfbyzGTa+NLeD18ICtoX/+H57JtJj3Fgorg+81CEn1CiqkU7T+EkL99WUir9SBXrCfgT+I7n485BXR8x866pZP5hU49j+VL8gV9jkw5Op3OuzNuonxQIq1HW3TOE6GJbe45aNa6F1Mpel10z4luMSOgvCTpIej0SgDXk7wIwKMA3gIAJLcAuNTMLk7nvwXghQBWkNwJ4CIzuxnAF0geC4AA7gJwabcOVQREtAL3pkcIUU+kVSHqzyB1amZ7AJzlWb4dwMUd869aYP0ze+1TzrFoAXr9KkQzkFaFqD/x61TOsYgevaoVohlIq0LUnzbotHbOcUhsqu9P4q7li9txtz3uabNkLBu7OMp8vOHkbD5uaNoZbf3gdD5Yeb9TXMA3uH+ReL6QUU18v2vRWO4mZnHO6VVtT3QrPOH/PntkeMJ5c7r0xe65bUY9xQXGnI374utWebb9b3tXZOZ9hUJ8y7rhO9+48Y3e+GrHbt/v2q0gC+DXZO+jHYVRtChJ8PYHqFWS6wB8GcApAB4B8BYz2+e02QzgkwBWAZgF8GEz+/LAjCyZfGGK7seXT6e5IiCe483VxchoPn9gxZIpT3/Z9aY8OTxuXo8vN8DN8wkhaD8C/AsAmHX691/znXOi51xWpHhHSM5HP8R+TW2inyNEjxiMcz1NQohhMHCthpSlPQzgt8zsRQDOBfAxkmv67ViI5hL/NbV2T46FKJs2vAISIgaGoNWuZWnN7F86Pv+E5C4AxwLYPxALhagZbbimyjkWrSD2V0BCxMKAtdpTWVqSpyOpsvVQ1YYJUWdiv6bKORYtwDQ8lBCNoJBWN5Dc3jG/1cy2zs+UVZY2LSTweQAXmJlOKKLFxH9NbYRznC9okQ80Hx/p/odyz3q+cHU3iWbdRD5Z4Kgn8P+AMwD5M54EghCqSqLxbTWkp5BEn6KB62UVd+mGAZhrYMxT03CTTWbn8n/fkD+5m0ji287YqDnz+b+vL5HviKPTQzOeKj8BuOcJ3/lnLCCx0MVfJCDfLuQ84Sb3+hJ9BlWIJ5SCWt1tZlsW3GYJZWlJrgLwjwDeZ2bf7dXAYeFLtjPnOHCTxoDAJO+cTn1XgmwCni+xbMyTpLd6WXZZSIGPSU//rk5dTfrwFQpx9e37fWY8/fvOXS6uTn2ajESnjaIRzrEQ/RL7KyAhYmHAWp0vS3slFihLS3ICwFcAfM7MbhikcULUldivqXKORQuw6IUsRBwMXKshZWnfAuDVANaTvDBd70Izu2uQhgpRH+K/pvY1lBvJc0k+QHIHSd8QOEIMHcN8PZ/wKTakVdEEBq1VM9tjZmeZ2SYzO9vM9qbLt6eOMczsr81s3Mw2d0x39b2zHqRT0QTacE0t/OSY5CiATwA4B8BOAHeQ3GZm9y24Drp747MF41CLDNzv7d+JJ/bFF/tY7ol5rIqQPXV/x9DfNbftmsU6FcMwh3xcW1sootUy8B1zRfQ95Sm6g+kiFuU5ZqykDZWGr5jIEMwYGu3V6qB06sa1z5b0c0/O5N0J37KyWOUpHjIoQgqnAMC44xcUy3AII9SmknqLXqf9eJSnA9hhZg+b2RSA65CMGSlE7Yj9LrcL0qpoDC3WqnQqGkPsOu3ntu5EAI91zO8E8PL+zBGifAwWfWZtF6RV0QharlXpVDSCNui08oQ8kpcAuAQAjp1YVXV3QniJ/RVQv3Tq9PglK4dsjWgz0urCdOp0o3QqhkjsOu0nrOJxACd3zJ+ULstgZlvNbIuZbVk9tryP7oQoikX/CqgLXbXaqdM149KpGBat1mpPOl07sWygxgnxU+LXaT9Pju8AsInkqUgEfD6Aty+2wo7DT+7+D3f8l0cBbACwu4++h0ETbQaaaXcvNj+3WwMDMGdx3+V2oSet/uiZp3a/4lsfbapOgWbaHbvNXXUKtF6rPen0voO7dm++7eOPprOxHz91oQ0265qKPpxjM5sh+U4ANwMYBXC1md3bZZ1jAYDk9sUqGtWRJtoMNNPu8m2Ov9TlYvSq1SbrFGim3bJ5nvZqtahOAR0/g0I2zxO/TvuKOTazmwDcVJItQlSGRR4f1Q1pVTSFNmtVOhVNIXadqkKeaAHxV/MRIg6kVSHqT/w6LadyRu9sHVK//dBEm4Fm2l2qzW2o5lMRTTx2gGbaLZshrfaBjp/BIJsxeJ2SXEfyFpIPpv+v9bTZTPI7JO8leTfJt3Z8dyrJ29PKk18mOdG1T7MYKqAJsTATY6vt+FW/3NM6j+27+c6mxZYJ0XSkVSHqz6B1SvJPAew1syvTsuprzewyp83zAZiZPUjyBAB3Avh5M9tP8noAf2dm15H8SwA/MLNPLtbnsJ4cCzFADGazPU1CiGEgrQpRfwau0/MAXJt+vhbAG3MWmf2LmT2Yfv4JgF0AjiVJAGcCuGGx9V0UcyxaQezxUULEgrQqRP0ZsE6PM7Mn0s9PAjhuscYkTwcwAeAhAOsB7DezmfTrnUiqUS7KwJ8ckzyX5ANp7Mflg+4/BJJXk9xF8p6OZV1jXoYJyZNJ3kbyvjTm5vfT5bW1m+RSkv9M8gepzR9Kl/ccH7Q4BsNsT1MJ+9ZXjNSwkU6rQTrtxuC12mSaoFNAWh0UNb+mbiC5vWO6xLH9ayTv8UznZXpOYoEXjAcmuRHA5wH872ZW2IMfqHNMchTAJwC8DsBpAN5G8rRB2hDINQDOdZZdDuBWM9sE4NZ0vk7MAPhDMzsNwCsA/G7629bZ7kkAZ5rZiwFsBnAuyVcA+AiAq8zseQD2Abion04MgNlcT1MJhPzuhwH8lpm9CMnx9jGSa8rovB+k00qRThdhSFptJA3SKSCtDoo6X1N3z1d3TKdMoqCZnW1mv+CZbgTwVOr0zju/u3x2kVwF4B8BvM/Mvpsu3gNgDcn5SAlvNWeXQT85Ph3ADjN72MymAFyHJJakVpjZNwHsdRZ3jXkZJmb2hJl9L/18EMD9SF4d1NZuS3gmnR1PJ0OB+KAuPWGux38lUDhGqozO+0Q6rQjptGtvw9BqU2mETgFpdVBEfE3dBuCC9PMFAG50G6RPw78C4HNmNr+v80+abwPwpsXWdxm0c3wigMc65oNiP2pCTzEvw4TkKQBeAuB21NxukqMk70LiGN6CJEao5/igRTEUSR5Y9BVQAP3ESA0b6XQASKceimm1rTRZp0DNj/lOpFWHwev0SgDnkHwQwNnpPEhuIfnptM1bALwawIUk70qnzel3lwF4N8kdSGKQP9OtQyXkFcDMjGQtx8AjuQLA3wL4AzN7muSz39XRbktUszkNJ/gKgBdW0EuRcRZ3dxt2huTXABzv+ep9md67/O4dMVIX9BMjJbLU8XifRzpdsCeNXdxC6njMzyOtensZqE7NbA+AszzLtwO4OP381wD+eoH1H0bypiWYQTvHjwM4uWM+KPajJjxFcqOZPbFYzMswITmORMRfMLO/SxfX3m4ASMcivA3ALyOND0rvdPs+Rubjo8rGzM5e6DuSQb/7AjFSw0Y6rRDpdJHtoxqtRkqTdQo04JiXVhfYNuLX6aDDKu4AsCnNnJwAcD6SWJIm0DXmZZgwuZ39DID7zeyjHV/V1m6Sx84noJFcBuAcJHFdPccHLc5QMuALx0jVAOm0IqTTbmi0ih5osk6BGh/zgLS6OPHrdOAV8ki+HsDHAIwCuNrMPjxQAwIg+SUAZwDYAOApAB8A8PcArgfwMwAeBfAWM3MTDIYGyV8B8C0APwSefd/xx0hipGppN8lfRJIcMIrkRu16M7uC5M8iSS5ZB+D7AH7TzCaL9jM2utxWLX9+T+vse+YHfVXdIrkent+d5BYAl5rZxSR/E8BnAdzbseqFZnZX0X7LQjqtBul0cYah1SbTBJ0C0uqgiPmaOmhUPlpEz9joclu57Hk9rbP/0A8bJWQhYkBaFaL+tEGnSsgT0WPpsDNCiHojrQpRf9qgUznHohXEnjwgRCxIq0LUn9h1KudYxI9Z28dDFaIZSKtC1J8W6FTOsWgFGjtViGYgrQpRf2LXqZxj0QIs+ldAQsSBtCpE/Ylfp3KORfS0YcByIWJAWhWi/rRBp3KORSuI/RWQELEgrQpRf2LXqZxj0QLifwUkRBxIq0LUn/h1KudYtILYhSxELEirQtSf2HUq51i0AAMifwUkRBxIq0LUn/h1KudYxI/Ff5crRBRIq0LUnxboVM6xiB5D/MkDQsSAtCpE/WmDTuUcixYQf/KAEHEgrQpRf+LXqZxj0RLiLnUpRDxIq0LUn7h1KudYtID473KFiANpVYj6E79O5RyLlhC3kIWIB2lViPoTt05Hhm2AENVjgM31NgkhhsBgtUpyHclbSD6Y/r/W0+a5JL9H8i6S95K8tK9OhWg8tdTpZpLfSTV6N8m3dnx3Dckfpxq+i+Tmbn3KORatwHr8J4QYDgPW6uUAbjWzTQBuTeddngDwy2a2GcDLAVxO8oR+OxaiydRQp4cB/JaZvQjAuQA+RnJNx/fvMbPN6XRXtw7lHIuWMNfjJIQYDgPV6nkArk0/XwvgjW4DM5sys8l0dgl03RQCNdTpv5jZg+nnnwDYBeDYoh1K5KIdmPU2CSGGw2C1epyZPZF+fhLAcb5GJE8meTeAxwB8JL34CtFeaqjTeUieDmACwEMdiz+chltcRXJJtw6VkCdagEIlhGgGhbS6geT2jvmtZrZ1fobk1wAc71nvfZmezYykt3MzewzAL6bhFH9P8gYze6pXQ4WIg3rqNN3ORgCfB3CB/XRIjfcicaonAGwFcBmAKxYzVs6xaAM3AzMbelxndyWWCCEWo5BWzezchb40s7MX+o7kUyQ3mtkT6UV112IdmdlPSN4D4FUAbujRTiFioZY6JbkKwD8CeJ+Zfbdj2/NPnSdJfhbAH3UzVs6xiJ7FBCmEqA9D0Oo2ABcAuDL9/0a3AcmTAOwxsyNplvyvALhqoFYKUSNqqtMJAF8B8Dkzu8H5bt6xJpJ45Xu6dUhTfKUQQogWQnI9gOsB/AyARwG8xcz2ktwC4FIzu5jkOQD+HIABIIC/6HwdLISolkCd/iaAzwK4t2PVC83sLpJfR5KcRwB3pes8s2ifco6FEEIIIYRI0GgVQgghhBBCpMg5FkIIIYQQIkXOsRBCCCGEEClyjoUQQgghhEiRcyyEEEIIIUSKnGMhhBBCCCFS5BwLIYQQQgiRIudYCCGEEEKIlP8ffKw1o5Zq/XoAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 720x288 with 6 Axes>"
       ]
      },
      "metadata": {
-      "needs_background": "light",
-      "tags": []
+      "needs_background": "light"
      },
      "output_type": "display_data"
     }
@@ -518,14 +539,13 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 720x432 with 4 Axes>"
       ]
      },
      "metadata": {
-      "needs_background": "light",
-      "tags": []
+      "needs_background": "light"
      },
      "output_type": "display_data"
     }
@@ -557,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -569,14 +589,13 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 720x432 with 12 Axes>"
       ]
      },
      "metadata": {
-      "needs_background": "light",
-      "tags": []
+      "needs_background": "light"
      },
      "output_type": "display_data"
     }
diff --git a/diffphys-code-sol.ipynb b/diffphys-code-sol.ipynb
index 2bba135..4708ee8 100644
--- a/diffphys-code-sol.ipynb
+++ b/diffphys-code-sol.ipynb
@@ -1,1417 +1,1392 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qT_RWmTEugu9"
-      },
-      "source": [
-        "# Reducing Numerical Errors with Deep Learning\n",
-        "\n",
-        "In this example we will target numerical errors that arise in the discretization of a continuous PDE $\\mathcal P^*$, i.e. when we formulate $\\mathcal P$. This approach will demonstrate that, despite the lack of closed-form descriptions, discretization errors often are functions with regular and repeating structures and, thus, can be learned by a neural network. Once the network is trained, it can be evaluated locally to improve the solution of a PDE-solver, i.e., to reduce its numerical error. The resulting method is a hybrid one: it will always run (a coarse) PDE solver, and then improve it at runtime with corrections inferred by an NN.\n",
-        "\n",
-        " \n",
-        "Pretty much all numerical methods contain some form of iterative process: repeated updates over time for explicit solvers, or within a single update step for implicit solvers. \n",
-        "An example for the second case could be found [here](https://github.com/tum-pbs/CG-Solver-in-the-Loop),\n",
-        "but below we'll target the first case, i.e. iterations over time.\n",
-        "[[run in colab]](https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/diffphys-code-sol.ipynb)\n",
-        "\n",
-        "\n",
-        "## Problem formulation\n",
-        "\n",
-        "In the context of reducing errors, it's crucial to have a _differentiable physics solver_, so that the learning process can take the reaction of the solver into account. This interaction is not possible with supervised learning or PINN training. Even small inference errors of a supervised NN accumulate over time, and lead to a data distribution that differs from the distribution of the pre-computed data. This distribution shift leads to sub-optimal results, or even cause blow-ups of the solver.\n",
-        "\n",
-        "In order to learn the error function, we'll consider two different discretizations of the same PDE $\\mathcal P^*$: \n",
-        "a _reference_ version, which we assume to be accurate, with a discretized version \n",
-        "$\\mathcal P_r$, and solutions $\\mathbf r \\in \\mathscr R$, where $\\mathscr R$ denotes the manifold of solutions of $\\mathcal P_r$.\n",
-        "In parallel to this, we have a less accurate approximation of the same PDE, which we'll refer to as the _source_ version, as this will be the solver that our NN should later on interact with. Analogously,\n",
-        "we have $\\mathcal P_s$ with solutions $\\mathbf s \\in \\mathscr S$.\n",
-        "After training, we'll obtain a _hybrid_ solver that uses $\\mathcal P_s$ in conjunction with a trained network to obtain improved solutions, i.e., solutions that are closer to the ones produced by $\\mathcal P_r$.\n",
-        "\n",
-        "```{figure} resources/diffphys-sol-manifolds.jpeg\n",
-        "---\n",
-        "height: 150px\n",
-        "name: diffphys-sol-manifolds\n",
-        "---\n",
-        "Visual overview of coarse and reference manifolds\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tayrJa7_ZzS_"
-      },
-      "source": [
-        "\n",
-        "Let's assume $\\mathcal{P}$ advances a solution by a time step $\\Delta t$, and let's denote $n$ consecutive steps by a superscript:\n",
-        "$\n",
-        "\\newcommand{\\pde}{\\mathcal{P}}\n",
-        "\\newcommand{\\pdec}{\\pde_{s}}\n",
-        "\\newcommand{\\vc}[1]{\\mathbf{s}_{#1}} \n",
-        "\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \n",
-        "\\newcommand{\\vcN}{\\vs}          \n",
-        "\\newcommand{\\project}{\\mathcal{T}}   \n",
-        "\\pdec^n ( \\mathcal{T} \\vr{t} ) = \\pdec(\\pdec(\\cdots \\pdec( \\mathcal{T} \\vr{t}  )\\cdots)) .\n",
-        "$ \n",
-        "The corresponding state of the simulation is\n",
-        "$\n",
-        "\\mathbf{s}_{t+n} = \\mathcal{P}^n ( \\mathcal{T} \\mathbf{r}_{t} ) .\n",
-        "$\n",
-        "Here we assume a mapping operator $\\mathcal{T}$ exists that transfers a reference solution to the source manifold. This could, e.g., be a simple downsampling operation.\n",
-        "Especially for longer sequences, i.e. larger $n$, the source state \n",
-        "$\\newcommand{\\vc}[1]{\\mathbf{s}_{#1}} \\vc{t+n}$\n",
-        "will deviate from a corresponding reference state\n",
-        "$\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \\vr{t+n}$. \n",
-        "This is what we will address with an NN in the following.\n",
-        "\n",
-        "As before, we'll use an $L^2$-norm to quantify the deviations, i.e., \n",
-        "an error function $\\newcommand{\\loss}{e} \n",
-        "\\newcommand{\\corr}{\\mathcal{C}} \n",
-        "\\newcommand{\\vc}[1]{\\mathbf{s}_{#1}} \n",
-        "\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \n",
-        "\\loss (\\vc{t},\\mathcal{T} \\vr{t})=\\Vert\\vc{t}-\\mathcal{T} \\vr{t}\\Vert_2$. \n",
-        "Our learning goal is to train at a correction operator \n",
-        "$\\mathcal{C} ( \\mathbf{s} )$ such that \n",
-        "a solution to which the correction is applied has a lower error than the original unmodified (source) \n",
-        "solution: $\\newcommand{\\loss}{e} \n",
-        "\\newcommand{\\corr}{\\mathcal{C}} \n",
-        "\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \n",
-        "\\loss ( \\mathcal{P}_{s}( \\corr (\\mathcal{T} \\vr{t}) ) , \\mathcal{T} \\vr{t+1}) < \\loss ( \\mathcal{P}_{s}( \\mathcal{T} \\vr{t} ), \\mathcal{T} \\vr{t+1})$. \n",
-        "\n",
-        "The correction function \n",
-        "$\\newcommand{\\vcN}{\\mathbf{s}} \\newcommand{\\corr}{\\mathcal{C}} \\corr (\\vcN | \\theta)$ \n",
-        "is represented as a deep neural network with weights $\\theta$\n",
-        "and receives the state $\\mathbf{s}$ to infer an additive correction field with the same dimension.\n",
-        "To distinguish the original states $\\mathbf{s}$ from the corrected ones, we'll denote the latter with an added tilde $\\tilde{\\mathbf{s}}$.\n",
-        "The overall learning goal now becomes\n",
-        "\n",
-        "$$\n",
-        "\\newcommand{\\corr}{\\mathcal{C}}  \n",
-        "\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \n",
-        "\\text{arg min}_\\theta \\big( ( \\mathcal{P}_{s} \\corr )^n ( \\mathcal{T} \\vr{t} ) - \\mathcal{T} \\vr{t+n} \\big)^2\n",
-        "$$\n",
-        "\n",
-        "To simplify the notation, we've dropped the sum over different samples here (the $i$ from previous versions).\n",
-        "A crucial bit that's easy to overlook in the equation above, is that the correction depends on the modified states, i.e.\n",
-        "it is a function of\n",
-        "$\\tilde{\\mathbf{s}}$, so we have \n",
-        "$\\newcommand{\\vctN}{\\tilde{\\mathbf{s}}} \\newcommand{\\corr}{\\mathcal{C}} \\corr (\\vctN | \\theta)$.\n",
-        "These states actually evolve over time when training. They don't exist beforehand.\n",
-        "\n",
-        "**TL;DR**:\n",
-        "We'll train a network $\\mathcal{C}$ to reduce the numerical errors of a simulator with a more accurate reference. It's crucial to have the _source_ solver realized as a differential physics operator, such that it provides gradients for an improved training of $\\mathcal{C}$.\n",
-        "\n",
-        "<br>\n",
-        "\n",
-        "---\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hPgwGkzYdIww"
-      },
-      "source": [
-        "## Getting started with the implementation\n",
-        "\n",
-        "The following replicates an experiment from [Solver-in-the-loop: learning from differentiable physics to interact with iterative pde-solvers](https://ge.in.tum.de/publications/2020-um-solver-in-the-loop/) {cite}`holl2019pdecontrol`, further details can be found in section B.1 of the [appendix](https://arxiv.org/pdf/2007.00016.pdf) of the paper.\n",
-        "\n",
-        "First, let's download the prepared data set (for details on generation & loading cf. https://github.com/tum-pbs/Solver-in-the-Loop), and let's get the data handling out of the way, so that we can focus on the _interesting_ parts..."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "JwZudtWauiGa",
-        "outputId": "d82af215-e4e6-40b3-bfbc-52c1013cfb74"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Loaded data, 6 training sims\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os, sys, logging, argparse, pickle, glob, random, distutils.dir_util, urllib.request\n",
-        "\n",
-        "fname_train = 'sol-karman-2d-train.pickle'\n",
-        "if not os.path.isfile(fname_train):\n",
-        "  print(\"Downloading training data (73MB), this can take a moment the first time...\")\n",
-        "  urllib.request.urlretrieve(\"https://physicsbaseddeeplearning.org/data/\"+fname_train, fname_train)\n",
-        "\n",
-        "with open(fname_train, 'rb') as f: data_preloaded = pickle.load(f)\n",
-        "print(\"Loaded data, {} training sims\".format(len(data_preloaded)) )\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RY1F4kdWPLNG"
-      },
-      "source": [
-        "Also let's get installing / importing all the necessary libraries out of the way. And while we're at it, we set the random seed - obviously, 42 is the ultimate choice here 🙂"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "id": "BGN4GqxkIueM"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install --upgrade --quiet phiflow==2.1\n",
-        "#!pip install --upgrade --quiet git+https://github.com/tum-pbs/PhiFlow@develop\n",
-        "\n",
-        "from phi.tf.flow import *\n",
-        "import tensorflow as tf\n",
-        "from tensorflow import keras\n",
-        "\n",
-        "random.seed(42) \n",
-        "np.random.seed(42)\n",
-        "tf.random.set_seed(42)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OhnzPdoww11P"
-      },
-      "source": [
-        "## Simulation setup\n",
-        "\n",
-        "Now we set up the _source_ simulation $\\mathcal{P}_{s}$. \n",
-        "Note that we won't deal with \n",
-        "$\\mathcal{P}_{r}$\n",
-        "below: the downsampled reference data is contained in the training data set. It was generated with a four times finer discretization. Below we're focusing on the interaction of the source solver and the NN. \n",
-        "\n",
-        "This code block and the next ones will define lots of functions, that will be used later on for training.\n",
-        "\n",
-        "The `KarmanFlow` solver below simulates a relatively standard wake flow case with a spherical obstacle in a rectangular domain, and an explicit viscosity solve to obtain different Reynolds numbers. This is the geometry of the setup:\n",
-        "\n",
-        "```{figure} resources/diffphys-sol-domain.png\n",
-        "---\n",
-        "height: 200px\n",
-        "name: diffphys-sol-domain\n",
-        "---\n",
-        "Domain setup for the wake flow case (sizes in the imlpementation are using an additional factor of 100).\n",
-        "```\n",
-        "\n",
-        "The solver applies inflow boundary conditions for the y-velocity with a pre-multiplied mask (`vel_BcMask`), to set the y components at the bottom of the domain during the simulation step. This mask is created with the `HardGeometryMask` from phiflow, which initializes the spatially shifted entries for the components of a staggered grid correctly. The simulation step is quite straight forward: it computes contributions for viscosity, inflow, advection and finally makes the resulting motion divergence free via an implicit pressure solve:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {
-        "id": "6WNMcdWUw4EP"
-      },
-      "outputs": [],
-      "source": [
-        "class KarmanFlow():\n",
-        "    def __init__(self, domain):\n",
-        "        self.domain = domain\n",
-        "\n",
-        "        self.vel_BcMask = self.domain.staggered_grid(HardGeometryMask(Box[:5, :]) )\n",
-        "    \n",
-        "        self.inflow = self.domain.scalar_grid(Box[5:10, 25:75])         # scale with domain if necessary!\n",
-        "        self.obstacles = [Obstacle(Sphere(center=[50, 50], radius=10))] \n",
-        "\n",
-        "    def step(self, density_in, velocity_in, re, res, buoyancy_factor=0, dt=1.0):\n",
-        "        velocity = velocity_in\n",
-        "        density = density_in\n",
-        "\n",
-        "        # viscosity\n",
-        "        velocity = phi.flow.diffuse.explicit(field=velocity, diffusivity=1.0/re*dt*res*res, dt=dt)\n",
-        "        \n",
-        "        # inflow boundary conditions\n",
-        "        velocity = velocity*(1.0 - self.vel_BcMask) + self.vel_BcMask * (1,0)\n",
-        "\n",
-        "        # advection \n",
-        "        density = advect.semi_lagrangian(density+self.inflow, velocity, dt=dt)\n",
-        "        velocity = advected_velocity = advect.semi_lagrangian(velocity, velocity, dt=dt)\n",
-        "\n",
-        "        # mass conservation (pressure solve)\n",
-        "        pressure = None\n",
-        "        velocity, pressure = fluid.make_incompressible(velocity, self.obstacles)\n",
-        "        self.solve_info = { 'pressure': pressure, 'advected_velocity': advected_velocity }\n",
-        "        \n",
-        "        return [density, velocity]\n",
-        "\n",
-        "    "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RYFUGICgxk0K"
-      },
-      "source": [
-        "## Network architecture\n",
-        "\n",
-        "We'll also define two alternative versions of a neural networks to represent \n",
-        "$\\newcommand{\\vcN}{\\mathbf{s}} \\newcommand{\\corr}{\\mathcal{C}} \\corr$. In both cases we'll use fully convolutional networks, i.e. networks without any fully-connected layers. We'll use Keras within tensorflow to define the layers of the network (mostly via `Conv2D`), typically activated via ReLU and LeakyReLU functions, respectively.\n",
-        "The inputs to the network are: \n",
-        "- 2 fields with x,y velocity\n",
-        "- the Reynolds number as constant channel.\n",
-        "\n",
-        "The output is: \n",
-        "- a 2 component field containing the x,y velocity.\n",
-        "\n",
-        "First, let's define a small network consisting only of four convolutional layers with ReLU activations (we're also using keras here for simplicity). The input dimensions are determined from input tensor in the `inputs_dict` (it has three channels: u,v, and Re). Then we process the data via three conv layers with 32 features each, before reducing to 2 channels in the output. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {
-        "id": "qIrWYTy6xscA"
-      },
-      "outputs": [],
-      "source": [
-        "def network_small(inputs_dict):\n",
-        "    l_input = keras.layers.Input(**inputs_dict)\n",
-        "    block_0 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_input)\n",
-        "    block_0 = keras.layers.LeakyReLU()(block_0)\n",
-        "\n",
-        "    l_conv1 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_0)\n",
-        "    l_conv1 = keras.layers.LeakyReLU()(l_conv1)\n",
-        "    l_conv2 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv1)\n",
-        "    block_1 = keras.layers.LeakyReLU()(l_conv2)\n",
-        "\n",
-        "    l_output = keras.layers.Conv2D(filters=2,  kernel_size=5, padding='same')(block_1) # u, v\n",
-        "    return keras.models.Model(inputs=l_input, outputs=l_output)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "YfHvdI7yxtdj"
-      },
-      "source": [
-        "For flexibility (and larger-scale tests later on), let's also define a _proper_ ResNet with a few more layers. This architecture is the one from the original paper, and will give a fairly good performance (`network_small` above will train faster, but give a sub-optimal performance at inference time)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {
-        "id": "TyfpA7Fbx0ro"
-      },
-      "outputs": [],
-      "source": [
-        "def network_medium(inputs_dict):\n",
-        "    l_input = keras.layers.Input(**inputs_dict)\n",
-        "    block_0 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_input)\n",
-        "    block_0 = keras.layers.LeakyReLU()(block_0)\n",
-        "\n",
-        "    l_conv1 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_0)\n",
-        "    l_conv1 = keras.layers.LeakyReLU()(l_conv1)\n",
-        "    l_conv2 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv1)\n",
-        "    l_skip1 = keras.layers.add([block_0, l_conv2])\n",
-        "    block_1 = keras.layers.LeakyReLU()(l_skip1)\n",
-        "\n",
-        "    l_conv3 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_1)\n",
-        "    l_conv3 = keras.layers.LeakyReLU()(l_conv3)\n",
-        "    l_conv4 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv3)\n",
-        "    l_skip2 = keras.layers.add([block_1, l_conv4])\n",
-        "    block_2 = keras.layers.LeakyReLU()(l_skip2)\n",
-        "\n",
-        "    l_conv5 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_2)\n",
-        "    l_conv5 = keras.layers.LeakyReLU()(l_conv5)\n",
-        "    l_conv6 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv5)\n",
-        "    l_skip3 = keras.layers.add([block_2, l_conv6])\n",
-        "    block_3 = keras.layers.LeakyReLU()(l_skip3)\n",
-        "\n",
-        "    l_conv7 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_3)\n",
-        "    l_conv7 = keras.layers.LeakyReLU()(l_conv7)\n",
-        "    l_conv8 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv7)\n",
-        "    l_skip4 = keras.layers.add([block_3, l_conv8])\n",
-        "    block_4 = keras.layers.LeakyReLU()(l_skip4)\n",
-        "\n",
-        "    l_conv9 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_4)\n",
-        "    l_conv9 = keras.layers.LeakyReLU()(l_conv9)\n",
-        "    l_convA = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv9)\n",
-        "    l_skip5 = keras.layers.add([block_4, l_convA])\n",
-        "    block_5 = keras.layers.LeakyReLU()(l_skip5)\n",
-        "\n",
-        "    l_output = keras.layers.Conv2D(filters=2,  kernel_size=5, padding='same')(block_5)\n",
-        "    return keras.models.Model(inputs=l_input, outputs=l_output)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ew-MgPSlyLW-"
-      },
-      "source": [
-        "Next, we're coming to two functions which are pretty important: they transform the simulation state into an input tensor for the network, and vice versa. Hence, they're the interface between _keras/tensorflow_ and _phiflow_.\n",
-        "\n",
-        "The `to_keras` function uses the two vector components via `vector['x']` and `vector['y']` to discard the outermost layer of the velocity field grids. This gives two tensors of equal size that are concatenated. \n",
-        "It then adds a constant channel via `math.ones` that is multiplied by the desired Reynolds number in `ext_const_channel`. The resulting stack of grids is stacked along the `channels` dimensions, and represents an  input to the neural network. \n",
-        "\n",
-        "After network evaluation, we transform the output tensor back into a phiflow grid via the `to_phiflow` function. \n",
-        "It converts the 2-component tensor that is returned by the network into a phiflow staggered grid object, so that it is compatible with the velocity field of the fluid simulation.\n",
-        "(Note: these are two _centered_ grids with different sizes, so we leave the work to the `domain.staggered_grid` function, which also sets physical size and boundary conditions as given by the domain object)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {
-        "id": "hhGFpTjGyRyg"
-      },
-      "outputs": [],
-      "source": [
-        "\n",
-        "def to_keras(dens_vel_grid_array, ext_const_channel):\n",
-        "    # align the sides the staggered velocity grid making its size the same as the centered grid\n",
-        "    return math.stack(\n",
-        "        [\n",
-        "            math.pad( dens_vel_grid_array[1].vector['x'].values, {'x':(0,1)} , math.extrapolation.ZERO),\n",
-        "            dens_vel_grid_array[1].vector['y'].y[:-1].values,         # v\n",
-        "            math.ones(dens_vel_grid_array[0].shape)*ext_const_channel # Re\n",
-        "        ],\n",
-        "        math.channel('channels')\n",
-        "    )\n",
-        "\n",
-        "def to_phiflow(tf_tensor, domain):\n",
-        "    return domain.staggered_grid(\n",
-        "        math.stack(\n",
-        "            [\n",
-        "                math.tensor(tf.pad(tf_tensor[..., 1], [(0,0), (0,1), (0,0)]), math.batch('batch'), math.spatial('y, x')), # v\n",
-        "                math.tensor( tf_tensor[...,:-1, 0], math.batch('batch'), math.spatial('y, x')), # u \n",
-        "            ], math.channel('vector')\n",
-        "        )\n",
-        "    )\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VngMwN_9y00S"
-      },
-      "source": [
-        "---\n",
-        "\n",
-        "## Data handling\n",
-        "\n",
-        "So far so good - we also need to take care of a few more mundane tasks, e.g., some data handling and randomization. Below we define a `Dataset` class that stores all \"ground truth\" reference data (already downsampled).\n",
-        "\n",
-        "We actually have a lot of data dimensions: multiple simulations, with many time steps, each with different fields. This makes the code below a bit more difficult to read.\n",
-        "\n",
-        "The data format for the numpy array `dataPreloaded`: is  `['sim_name', frame, field (dens & vel)]`, where each field has dimension `[batch-size, y-size, x-size, channels]` (this is the standard for a phiflow export)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {
-        "id": "tjywcdD2y20t"
-      },
-      "outputs": [],
-      "source": [
-        "class Dataset():\n",
-        "    def __init__(self, data_preloaded, num_frames, num_sims=None, batch_size=1, is_testset=False):\n",
-        "        self.epoch         = None\n",
-        "        self.epochIdx      = 0\n",
-        "        self.batch         = None\n",
-        "        self.batchIdx      = 0\n",
-        "        self.step          = None\n",
-        "        self.stepIdx       = 0\n",
-        "\n",
-        "        self.dataPreloaded = data_preloaded\n",
-        "        self.batchSize     = batch_size\n",
-        "\n",
-        "        self.numSims       = num_sims\n",
-        "        self.numBatches    = num_sims//batch_size\n",
-        "        self.numFrames     = num_frames\n",
-        "        self.numSteps      = num_frames\n",
-        "        \n",
-        "        # initialize directory keys (using naming scheme from SoL codebase)\n",
-        "        # constant additional per-sim channel: Reynolds numbers from data generation\n",
-        "        # hard coded for training and test data here\n",
-        "        if not is_testset:\n",
-        "            self.dataSims = ['karman-fdt-hires-set/sim_%06d'%i for i in range(num_sims) ]\n",
-        "            ReNrs = [160000.0, 320000.0, 640000.0,  1280000.0,  2560000.0,  5120000.0]\n",
-        "            self.extConstChannelPerSim = { self.dataSims[i]:[ReNrs[i]] for i in range(num_sims) }\n",
-        "        else:\n",
-        "            self.dataSims = ['karman-fdt-hires-testset/sim_%06d'%i for i in range(num_sims) ]\n",
-        "            ReNrs = [120000.0, 480000.0, 1920000.0, 7680000.0] \n",
-        "            self.extConstChannelPerSim = { self.dataSims[i]:[ReNrs[i]] for i in range(num_sims) }\n",
-        "\n",
-        "        self.dataFrames = [ np.arange(num_frames) for _ in self.dataSims ]  \n",
-        "\n",
-        "        # debugging example, check shape of a single marker density field:\n",
-        "        #print(format(self.dataPreloaded[self.dataSims[0]][0][0].shape )) \n",
-        "        \n",
-        "        # the data has the following shape ['sim', frame, field (dens/vel)] where each field is [batch-size, y-size, x-size, channels]\n",
-        "        self.resolution = self.dataPreloaded[self.dataSims[0]][0][0].shape[1:3]  \n",
-        "\n",
-        "        # compute data statistics for normalization\n",
-        "        self.dataStats = {\n",
-        "            'std': (\n",
-        "                np.std(np.concatenate([np.absolute(self.dataPreloaded[asim][i][0].reshape(-1)) for asim in self.dataSims for i in range(num_frames)], axis=-1)), # density\n",
-        "                np.std(np.concatenate([np.absolute(self.dataPreloaded[asim][i][1].reshape(-1)) for asim in self.dataSims for i in range(num_frames)], axis=-1)), # x-velocity\n",
-        "                np.std(np.concatenate([np.absolute(self.dataPreloaded[asim][i][2].reshape(-1)) for asim in self.dataSims for i in range(num_frames)], axis=-1)), # y-velocity\n",
-        "            )\n",
-        "        }\n",
-        "        self.dataStats.update({\n",
-        "            'ext.std': [ np.std([np.absolute(self.extConstChannelPerSim[asim][0]) for asim in self.dataSims]) ] # Reynolds Nr\n",
-        "        })\n",
-        "\n",
-        "        \n",
-        "        if not is_testset:\n",
-        "            print(\"Data stats: \"+format(self.dataStats))\n",
-        "\n",
-        "\n",
-        "    # re-shuffle data for next epoch\n",
-        "    def newEpoch(self, exclude_tail=0, shuffle_data=True):\n",
-        "        self.numSteps = self.numFrames - exclude_tail\n",
-        "        simSteps = [ (asim, self.dataFrames[i][0:(len(self.dataFrames[i])-exclude_tail)]) for i,asim in enumerate(self.dataSims) ]\n",
-        "        sim_step_pair = []\n",
-        "        for i,_ in enumerate(simSteps):\n",
-        "            sim_step_pair += [ (i, astep) for astep in simSteps[i][1] ]  # (sim_idx, step) ...\n",
-        "\n",
-        "        if shuffle_data: random.shuffle(sim_step_pair)\n",
-        "        self.epoch = [ list(sim_step_pair[i*self.numSteps:(i+1)*self.numSteps]) for i in range(self.batchSize*self.numBatches) ]\n",
-        "        self.epochIdx += 1\n",
-        "        self.batchIdx = 0\n",
-        "        self.stepIdx = 0\n",
-        "\n",
-        "    def nextBatch(self):  \n",
-        "        self.batchIdx += self.batchSize\n",
-        "        self.stepIdx = 0\n",
-        "\n",
-        "    def nextStep(self):\n",
-        "        self.stepIdx += 1\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "twIMJ3V0N1FX"
-      },
-      "source": [
-        "The `nextEpoch`, `nextBatch`, and `nextStep` functions will be called at training time to randomize the order of the training data.\n",
-        "\n",
-        "Now we need one more function that compiles the data for a mini batch to train with, called `getData` below. It returns batches of the desired size in terms of marker density, velocity, and Reynolds number.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "id": "Dfwd4TnqN1Tn"
-      },
-      "outputs": [],
-      "source": [
-        "# for class Dataset():\n",
-        "def getData(self, consecutive_frames):\n",
-        "    d_hi = [\n",
-        "        np.concatenate([\n",
-        "            self.dataPreloaded[\n",
-        "                self.dataSims[self.epoch[self.batchIdx+i][self.stepIdx][0]] # sim_key\n",
-        "            ][\n",
-        "                self.epoch[self.batchIdx+i][self.stepIdx][1]+j # frames\n",
-        "            ][0]\n",
-        "            for i in range(self.batchSize)\n",
-        "        ], axis=0) for j in range(consecutive_frames+1)\n",
-        "    ]\n",
-        "    u_hi = [\n",
-        "        np.concatenate([\n",
-        "            self.dataPreloaded[\n",
-        "                self.dataSims[self.epoch[self.batchIdx+i][self.stepIdx][0]] # sim_key\n",
-        "            ][\n",
-        "                self.epoch[self.batchIdx+i][self.stepIdx][1]+j # frames\n",
-        "            ][1]\n",
-        "            for i in range(self.batchSize)\n",
-        "        ], axis=0) for j in range(consecutive_frames+1)\n",
-        "    ]\n",
-        "    v_hi = [\n",
-        "        np.concatenate([\n",
-        "            self.dataPreloaded[\n",
-        "                self.dataSims[self.epoch[self.batchIdx+i][self.stepIdx][0]] # sim_key\n",
-        "            ][\n",
-        "                self.epoch[self.batchIdx+i][self.stepIdx][1]+j # frames\n",
-        "            ][2]\n",
-        "            for i in range(self.batchSize)\n",
-        "        ], axis=0) for j in range(consecutive_frames+1)\n",
-        "    ]\n",
-        "    ext = [\n",
-        "        self.extConstChannelPerSim[\n",
-        "            self.dataSims[self.epoch[self.batchIdx+i][self.stepIdx][0]]\n",
-        "        ][0] for i in range(self.batchSize)\n",
-        "    ]\n",
-        "    return [d_hi, u_hi, v_hi, ext]\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bIWnyPYlz8q7"
-      },
-      "source": [
-        "Note that the `density` here denotes a passively advected marker field, and not the density of the fluid. Below we'll be focusing on the velocity only, the marker density is tracked purely for visualization purposes.\n",
-        "\n",
-        "After all the definitions we can finally run some code. We define the dataset object with the downloaded data from the first cell."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "59EBdEdj0QR2",
-        "outputId": "aecfbbc6-d4ee-41c1-e92f-f1caf2d5c4a5"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Data stats: {'std': (2.6542656, 0.23155601, 0.3066732), 'ext.std': [1732512.6262166172]}\n"
-          ]
-        }
-      ],
-      "source": [
-        "nsims = 6\n",
-        "batch_size = 3\n",
-        "simsteps = 500\n",
-        "\n",
-        "dataset = Dataset( data_preloaded=data_preloaded, num_frames=simsteps, num_sims=nsims, batch_size=batch_size )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0N92RooWPzeA"
-      },
-      "source": [
-        "Additionally, we've defined several global variables to control the training and the simulation in the next code cells.\n",
-        "\n",
-        "The most important and interesting one is `msteps`. It defines the number of simulation steps that are unrolled at each training iteration. This directly influences the runtime of each training step, as we first have to simulate all steps forward, and then backpropagate the gradient through all `msteps` simulation steps interleaved with the NN evaluations. However, this is where we'll receive important feedback in terms of gradients how the inferred corrections actually influence a running simulation. Hence, larger `msteps` are typically better.\n",
-        "\n",
-        "In addition we define the resolution of the simulation in `source_res`, and allocate the fluid solver object called `simulator`. In order to create grids, it requires access to a `Domain` object, which mostly exists for convenience purposes: it stores resolution, physical size in `bounds`, and boundary conditions of the domain. This information needs to be passed to every grid, and hence it's convenient to have it in one place in the form of the `Domain`. For the setup described above, we need different boundary conditions along x and y: closed walls, and free flow in and out of the domain, respecitvely.\n",
-        "\n",
-        "We also instantiate the actual NN `network` in the next cell. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "EjgkdCzKP2Ip",
-        "outputId": "e38b8b33-7d6f-40e8-ce64-0250c908db7a"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Model: \"model_1\"\n",
-            "_________________________________________________________________\n",
-            " Layer (type)                Output Shape              Param #   \n",
-            "=================================================================\n",
-            " input_2 (InputLayer)        [(None, 64, 32, 3)]       0         \n",
-            "                                                                 \n",
-            " conv2d_4 (Conv2D)           (None, 64, 32, 32)        2432      \n",
-            "                                                                 \n",
-            " leaky_re_lu_3 (LeakyReLU)   (None, 64, 32, 32)        0         \n",
-            "                                                                 \n",
-            " conv2d_5 (Conv2D)           (None, 64, 32, 32)        25632     \n",
-            "                                                                 \n",
-            " leaky_re_lu_4 (LeakyReLU)   (None, 64, 32, 32)        0         \n",
-            "                                                                 \n",
-            " conv2d_6 (Conv2D)           (None, 64, 32, 32)        25632     \n",
-            "                                                                 \n",
-            " leaky_re_lu_5 (LeakyReLU)   (None, 64, 32, 32)        0         \n",
-            "                                                                 \n",
-            " conv2d_7 (Conv2D)           (None, 64, 32, 2)         1602      \n",
-            "                                                                 \n",
-            "=================================================================\n",
-            "Total params: 55,298\n",
-            "Trainable params: 55,298\n",
-            "Non-trainable params: 0\n",
-            "_________________________________________________________________\n"
-          ]
-        }
-      ],
-      "source": [
-        "# one of the most crucial! how many simulation steps to look into the future while training\n",
-        "msteps = 4\n",
-        "\n",
-        "# # this is the actual resolution in terms of cells\n",
-        "source_res = list(dataset.resolution)\n",
-        "# # this is a virtual size, in terms of abstract units for the bounding box of the domain (it's important for conversions or when rescaling to physical units)\n",
-        "simulation_length = 100.\n",
-        "\n",
-        "# for readability\n",
-        "from phi.physics._boundaries import Domain, OPEN, STICKY as CLOSED\n",
-        "\n",
-        "boundary_conditions = {\n",
-        "    'x':(phi.physics._boundaries.STICKY,phi.physics._boundaries.STICKY), \n",
-        "    'y':(phi.physics._boundaries.OPEN,  phi.physics._boundaries.OPEN) }\n",
-        "\n",
-        "domain = Domain(y=source_res[0], x=source_res[1], bounds=Box[0:2*simulation_length, 0:simulation_length], boundaries=boundary_conditions)\n",
-        "simulator = KarmanFlow(domain=domain)\n",
-        "\n",
-        "network = network_small(dict(shape=(source_res[0],source_res[1], 3)))\n",
-        "network.summary()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AbpNPzplQZMF"
-      },
-      "source": [
-        "## Interleaving simulation and NN\n",
-        "\n",
-        "Now comes the **most crucial** step in the whole setup: we define a function that encapsulates the chain of simulation steps and network evaluations in each training step. After all the work defining helper functions, it's actually pretty simple: we create a gradient tape via `tf.GradientTape()` such that we can backpropagate later on. We then loop over `msteps`, call the simulator via `simulator.step` for an input state, and afterwards evaluate the correction via `network(to_keras(...))`. The NN correction is then added to the last simulation state in the `prediction` list (we're actually simply overwriting the last simulated velocity `prediction[-1][1]` with `prediction[-1][1] + correction[-1]`.\n",
-        "\n",
-        "One other important thing that's happening here is normalization: the inputs to the network are divided by the standard deviations in `dataset.dataStats`. After evaluating the `network`, we only have a velocity left, so we simply multiply it by the standard deviation of the velocity again (via `* dataset.dataStats['std'][1]` and `[2]`).\n",
-        "\n",
-        "The `training_step` function also directly evaluates and returns the loss. Here, we simply use an $L^2$ loss over the whole sequence, i.e. the iteration over `msteps`. This is requiring a few lines of code because we separately loop over 'x' and 'y' components, in order to normalize and compare to the ground truth values from the training data set.\n",
-        "\n",
-        "The \"learning\" happens in the last two lines via `tape.gradient()` and `opt.apply_gradients()`, which then contain the aggregated information about how to change the NN weights to nudge the simulation closer to the reference for the full chain of simulation steps."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "id": "D5NeMcLGQaxh",
-        "scrolled": true
-      },
-      "outputs": [],
-      "source": [
-        "def training_step(dens_gt, vel_gt, Re, i_step):\n",
-        "    with tf.GradientTape() as tape:\n",
-        "        prediction, correction = [ [dens_gt[0],vel_gt[0]] ], [0] # predicted states with correction, inferred velocity corrections\n",
-        "\n",
-        "        for i in range(msteps):\n",
-        "            prediction += [\n",
-        "                simulator.step(\n",
-        "                    density_in=prediction[-1][0],\n",
-        "                    velocity_in=prediction[-1][1],\n",
-        "                    re=Re, res=source_res[1],\n",
-        "                )\n",
-        "            ]       # prediction: [[density1, velocity1], [density2, velocity2], ...]\n",
-        "\n",
-        "            model_input = to_keras(prediction[-1], Re)\n",
-        "            model_input /= math.tensor([dataset.dataStats['std'][1], dataset.dataStats['std'][2], dataset.dataStats['ext.std'][0]], channel('channels')) # [u, v, Re]\n",
-        "            model_out = network(model_input.native(['batch', 'y', 'x', 'channels']), training=True)\n",
-        "            model_out *= [dataset.dataStats['std'][1], dataset.dataStats['std'][2]] # [u, v]\n",
-        "            correction += [ to_phiflow(model_out, domain) ]                         # [velocity_correction1, velocity_correction2, ...]\n",
-        "\n",
-        "            prediction[-1][1] = prediction[-1][1] + correction[-1]\n",
-        "            #prediction[-1][1] = correction[-1]\n",
-        "\n",
-        "        # evaluate loss\n",
-        "        loss_steps_x = [\n",
-        "            tf.nn.l2_loss(\n",
-        "                (\n",
-        "                    vel_gt[i].vector['x'].values.native(('batch', 'y', 'x'))\n",
-        "                    - prediction[i][1].vector['x'].values.native(('batch', 'y', 'x'))\n",
-        "                )/dataset.dataStats['std'][1]\n",
-        "            )\n",
-        "            for i in range(1,msteps+1)\n",
-        "        ]\n",
-        "        loss_steps_x_sum = tf.math.reduce_sum(loss_steps_x)\n",
-        "\n",
-        "        loss_steps_y = [\n",
-        "            tf.nn.l2_loss(\n",
-        "                (\n",
-        "                    vel_gt[i].vector['y'].values.native(('batch', 'y', 'x'))\n",
-        "                    - prediction[i][1].vector['y'].values.native(('batch', 'y', 'x'))\n",
-        "                )/dataset.dataStats['std'][2]\n",
-        "            )\n",
-        "            for i in range(1,msteps+1)\n",
-        "        ]\n",
-        "        loss_steps_y_sum = tf.math.reduce_sum(loss_steps_y)\n",
-        "\n",
-        "        loss = (loss_steps_x_sum + loss_steps_y_sum)/msteps\n",
-        "\n",
-        "        gradients = tape.gradient(loss, network.trainable_variables)\n",
-        "        opt.apply_gradients(zip(gradients, network.trainable_variables))\n",
-        "\n",
-        "        return math.tensor(loss)    \n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c4yLlDM3QfUR"
-      },
-      "source": [
-        "Once defined, we prepare this function for executing the training step by calling phiflow's `math.jit_compile()` function. It automatically maps to the correct pre-compilation step of the chosen backend. E.g., for TF this internally creates a computational graph, and optimizes the chain of operations. For JAX, it can even compile optimized GPU code (if JAX is set up correctly). Thus, using the jit compilation can make a huge difference in terms of runtime."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {
-        "id": "K2JcO3-QQgC9"
-      },
-      "outputs": [],
-      "source": [
-        "\n",
-        "training_step_jit = math.jit_compile(training_step)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "E6Vly1_0QhZ1"
-      },
-      "source": [
-        "## Training\n",
-        "\n",
-        "For the training, we use a standard Adam optimizer, and run 15 epochs by default. This should be increased for the larger network or to obtain more accurate results. For longer training runs, it would also be beneficial to decrease the learning rate over the course of the epochs, but for simplicity, we'll keep `LR` constant here.\n",
-        "\n",
-        "Optionally, this is also the right point to load a network state to resume training."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {
-        "id": "PuljFamYQksW"
-      },
-      "outputs": [],
-      "source": [
-        "LR = 1e-4\n",
-        "EPOCHS = 15\n",
-        "\n",
-        "opt = tf.keras.optimizers.Adam(learning_rate=LR) \n",
-        "\n",
-        "# optional, load existing network...\n",
-        "# set to epoch nr. to load existing network from there\n",
-        "resume = 0\n",
-        "if resume>0: \n",
-        "    ld_network = keras.models.load_model('./nn_epoch{:04d}.h5'.format(resume)) \n",
-        "    #ld_network = keras.models.load_model('./nn_final.h5') # or the last one\n",
-        "    network.set_weights(ld_network.get_weights())\n",
-        "    "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lrALctV1RWBO"
-      },
-      "source": [
-        "Finally, we can start training the NN! This is very straight forward now, we simply loop over the desired number of iterations, get a batch each time via `getData`, feed it into the source simulation input `source_in`, and compare it in the loss with the `reference` data for the batch.\n",
-        "\n",
-        "The setup above will automatically take care that the differentiable physics solver used here provides the right gradient information, and provides it to the tensorflow network. Be warned: due to the complexity of the setup, this training run can take a while... (If you have a saved `nn_final.h5` network from a previous run, you can potentially skip this block and load the previously trained model instead via the cell above.)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "m3Nd8YyHRVFQ",
-        "outputId": "a9ce981d-cb10-4543-8eb1-0fd820c76e40",
-        "scrolled": true
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "epoch 001/015, batch 001/002, step 0001/0496: loss=2607.0625\n",
-            "epoch 001/015, batch 001/002, step 0002/0496: loss=1486.0303955078125\n",
-            "epoch 001/015, batch 001/002, step 0003/0496: loss=791.0106201171875\n",
-            "epoch 001/015, batch 001/002, step 0129/0496: loss=98.65435028076172\n",
-            "epoch 001/015, batch 001/002, step 0257/0496: loss=75.35194396972656\n",
-            "epoch 001/015, batch 001/002, step 0385/0496: loss=70.05856323242188\n",
-            "epoch 002/015, batch 001/002, step 0401/0496: loss=19.132190704345703\n",
-            "epoch 003/015, batch 001/002, step 0401/0496: loss=9.645946502685547\n",
-            "epoch 004/015, batch 001/002, step 0401/0496: loss=7.916687965393066\n",
-            "epoch 005/015, batch 001/002, step 0401/0496: loss=3.710268497467041\n",
-            "epoch 006/015, batch 001/002, step 0401/0496: loss=3.1778054237365723\n",
-            "epoch 007/015, batch 001/002, step 0401/0496: loss=2.8747799396514893\n",
-            "epoch 008/015, batch 001/002, step 0401/0496: loss=3.5371036529541016\n",
-            "epoch 009/015, batch 001/002, step 0401/0496: loss=1.6915209293365479\n",
-            "epoch 010/015, batch 001/002, step 0401/0496: loss=1.6486291885375977\n",
-            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
-            "epoch 011/015, batch 001/002, step 0401/0496: loss=1.92047119140625\n",
-            "epoch 012/015, batch 001/002, step 0401/0496: loss=2.0499801635742188\n",
-            "epoch 013/015, batch 001/002, step 0401/0496: loss=1.4348883628845215\n",
-            "epoch 014/015, batch 001/002, step 0401/0496: loss=1.2719428539276123\n",
-            "epoch 015/015, batch 001/002, step 0401/0496: loss=1.267827033996582\n",
-            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
-            "Training done, saved NN\n"
-          ]
-        }
-      ],
-      "source": [
-        "steps = 0\n",
-        "for j in range(EPOCHS):  # training\n",
-        "    dataset.newEpoch(exclude_tail=msteps)\n",
-        "    if j<resume:\n",
-        "        print('resume: skipping {} epoch'.format(j+1))\n",
-        "        steps += dataset.numSteps*dataset.numBatches\n",
-        "        continue\n",
-        "\n",
-        "    for ib in range(dataset.numBatches):   \n",
-        "        for i in range(dataset.numSteps): \n",
-        "\n",
-        "            # batch: [[dens0, dens1, ...], [x-velo0, x-velo1, ...], [y-velo0, y-velo1, ...], [ReynoldsNr(s)]]            \n",
-        "            batch = getData(dataset, consecutive_frames=msteps)\n",
-        "            \n",
-        "            dens_gt = [   # [density0:CenteredGrid, density1, ...]\n",
-        "                domain.scalar_grid(\n",
-        "                    math.tensor(batch[0][k], math.batch('batch'), math.spatial('y, x'))\n",
-        "                ) for k in range(msteps+1)\n",
-        "            ]\n",
-        "\n",
-        "            vel_gt = [   # [velocity0:StaggeredGrid, velocity1, ...]\n",
-        "                domain.staggered_grid(\n",
-        "                    math.stack(\n",
-        "                        [\n",
-        "                            math.tensor(batch[2][k], math.batch('batch'), math.spatial('y, x')),\n",
-        "                            math.tensor(batch[1][k], math.batch('batch'), math.spatial('y, x')),\n",
-        "                        ], math.channel('vector')\n",
-        "                    )\n",
-        "                ) for k in range(msteps+1)\n",
-        "            ]\n",
-        "            re_nr = math.tensor(batch[3], math.batch('batch'))\n",
-        "\n",
-        "            loss = training_step_jit(dens_gt, vel_gt, re_nr, math.tensor(steps)) \n",
-        "            \n",
-        "            steps += 1\n",
-        "            if (j==0 and ib==0 and i<3) or (j==0 and ib==0 and i%128==0) or (j>0 and ib==0 and i==400): # reduce output \n",
-        "              print('epoch {:03d}/{:03d}, batch {:03d}/{:03d}, step {:04d}/{:04d}: loss={}'.format( j+1, EPOCHS, ib+1, dataset.numBatches, i+1, dataset.numSteps, loss ))\n",
-        "            \n",
-        "            dataset.nextStep()\n",
-        "\n",
-        "        dataset.nextBatch()\n",
-        "\n",
-        "    if j%10==9: network.save('./nn_epoch{:04d}.h5'.format(j+1))\n",
-        "\n",
-        "# all done! save final version\n",
-        "network.save('./nn_final.h5'); print(\"Training done, saved NN\")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "swG7GeDpWT_Z"
-      },
-      "source": [
-        "The loss should go down from above 1000 initially to below 10. This is a good sign, but of course it's even more important to see how the NN-solver combination fares on new inputs. With this training approach we've realized a hybrid solver, consisting of a regular _source_ simulator, and a network that was trained to specifically interact with this simulator for a chosen domain of simulation cases.\n",
-        "\n",
-        "Let's see how well this works by applying it to a set of test data inputs with new Reynolds numbers that were not part of the training data.\n",
-        "\n",
-        "To keep things somewhat simple, we won't aim for a high-performance version of our hybrid solver. For performance, please check out the external code base: the network trained here should be directly useable in [this apply script](https://github.com/tum-pbs/Solver-in-the-Loop/blob/master/karman-2d/karman_apply.py).\n",
-        "\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0c38ne0UdIxV"
-      },
-      "source": [
-        "## Evaluation \n",
-        "\n",
-        "In order to evaluate the performance of our DL-powered solver, we essentially only need to repeat the inner loop of each training iteration for more steps. While we were limited to `msteps` evaluations at training time, we can now run our solver for arbitrary lengths. This is a good test for how well our solver has learned to keep the data within the desired distribution, and represents a generalization test for longer rollouts.\n",
-        "\n",
-        "We reuse the solver code from above, but in the following, we will consider two simulated versions: for comparison, we'll run one reference simulation in the _source_ space (i.e., without any modifications). This version receives the regular outputs of each evaluation of the simulator, and ignores the learned correction (stored in `steps_source` below). The second version, repeatedly computes the source solver plus the learned correction, and advances this state in the solver (`steps_hybrid`).\n",
-        "\n",
-        "We also need a set of new data. Below, we'll download a new set of Reynolds numbers (in between the ones used for training), on which we will later on run the unmodified simulator and the DL-powered one.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "RumKebW_05xp",
-        "outputId": "30cd6bab-d132-427a-e5d1-bccc681fa7b0"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Downloading test data (38MB), this can take a moment the first time...\n",
-            "Loaded test data, 4 training sims\n"
-          ]
-        }
-      ],
-      "source": [
-        "fname_test = 'sol-karman-2d-test.pickle'\n",
-        "if not os.path.isfile(fname_test):\n",
-        "  print(\"Downloading test data (38MB), this can take a moment the first time...\")\n",
-        "  urllib.request.urlretrieve(\"https://physicsbaseddeeplearning.org/data/\"+fname_test, fname_test)\n",
-        "\n",
-        "with open(fname_test, 'rb') as f: data_test_preloaded = pickle.load(f)\n",
-        "print(\"Loaded test data, {} training sims\".format(len(data_test_preloaded)) )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rZ9h-gRddIxb"
-      },
-      "source": [
-        "Next we create a new dataset object `dataset_test` that organizes the data. We're simply using the first batch of the unshuffled dataset, though.\n",
-        "\n",
-        "A subtle but important point: we still have to use the normalization from the original training data set: `dataset.dataStats['std']` values. The test data set has it's own mean and standard deviation, and so the trained NN never saw this data before. The NN was trained with the data in `dataset` above, and hence we have to use the constants from there for normalization to make sure the network receives values that it can relate to the data it was trained with."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "9OPruTGMdIxe",
-        "outputId": "1b5ad04d-f6ee-41f1-b94d-afee4b79f14a"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Reynolds numbers in test data set: (\u001b[94m120000.0\u001b[0m, \u001b[94m480000.0\u001b[0m, \u001b[94m1920000.0\u001b[0m, \u001b[94m7680000.0\u001b[0m) along \u001b[92mbatchᵇ\u001b[0m\n"
-          ]
-        }
-      ],
-      "source": [
-        "dataset_test = Dataset( data_preloaded=data_test_preloaded, is_testset=True, num_frames=simsteps, num_sims=4, batch_size=4 )\n",
-        "\n",
-        "# we only need 1 batch with t=0 states to initialize the test simulations with\n",
-        "dataset_test.newEpoch(shuffle_data=False)\n",
-        "batch = getData(dataset_test, consecutive_frames=0) \n",
-        "\n",
-        "re_nr_test = math.tensor(batch[3], math.batch('batch')) # Reynolds numbers\n",
-        "print(\"Reynolds numbers in test data set: \"+format(re_nr_test))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sMqRPg2pdIxh"
-      },
-      "source": [
-        "Next we construct a `math.tensor` as initial state for the centered marker fields, and a staggered grid from the next two indices of the test set batch. Similar to `to_phiflow` above, we use `phi.math.stack()` to combine two fields of appropriate size as a staggered grid."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "id": "xK1MEaPqdIxi"
-      },
-      "outputs": [],
-      "source": [
-        "source_dens_initial = math.tensor( batch[0][0], math.batch('batch'), math.spatial('y, x'))\n",
-        "\n",
-        "source_vel_initial = domain.staggered_grid(phi.math.stack([\n",
-        "    math.tensor(batch[2][0], math.batch('batch'),math.spatial('y, x')),\n",
-        "    math.tensor(batch[1][0], math.batch('batch'),math.spatial('y, x'))], channel('vector')))\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KhGVceo6dIxl"
-      },
-      "source": [
-        "Now we first run the _source_ simulation for 120 steps as baseline:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "nbTTl15kdIxl",
-        "outputId": "43c6ba5e-0152-4176-a037-1b38ab5f42dc"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Source simulation steps 121\n"
-          ]
-        }
-      ],
-      "source": [
-        "source_dens_test, source_vel_test = source_dens_initial, source_vel_initial\n",
-        "steps_source = [[source_dens_test,source_vel_test]]\n",
-        "\n",
-        "# note - math.jit_compile() not useful for numpy solve... hence not necessary\n",
-        "for i in range(120):\n",
-        "    [source_dens_test,source_vel_test] = simulator.step(\n",
-        "        density_in=source_dens_test,\n",
-        "        velocity_in=source_vel_test,\n",
-        "        re=re_nr_test,\n",
-        "        res=source_res[1],\n",
-        "    )\n",
-        "    steps_source.append( [source_dens_test,source_vel_test] )\n",
-        "\n",
-        "print(\"Source simulation steps \"+format(len(steps_source)))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vQV0qV5pdIxm"
-      },
-      "source": [
-        "Next, we compute the corresponding states of our learned hybrid solver. Here, we closely follow the training code, however, now without any gradient tapes or loss computations. We only evaluate the NN in a forward pass for each simulated state to compute a correction field:\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fH5tFfh9dIxn",
-        "outputId": "65f77439-b20d-4855-f084-25393393934d"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Steps with hybrid solver 121\n"
-          ]
-        }
-      ],
-      "source": [
-        "source_dens_test, source_vel_test = source_dens_initial, source_vel_initial\n",
-        "steps_hybrid = [[source_dens_test,source_vel_test]]\n",
-        "        \n",
-        "for i in range(120):\n",
-        "    [source_dens_test,source_vel_test] = simulator.step(\n",
-        "        density_in=source_dens_test,\n",
-        "        velocity_in=source_vel_test,\n",
-        "        re=math.tensor(re_nr_test),\n",
-        "        res=source_res[1],\n",
-        "    )\n",
-        "    model_input = to_keras([source_dens_test,source_vel_test], re_nr_test )\n",
-        "    model_input /= math.tensor([dataset.dataStats['std'][1], dataset.dataStats['std'][2], dataset.dataStats['ext.std'][0]], channel('channels')) # [u, v, Re]\n",
-        "    model_out = network(model_input.native(['batch', 'y', 'x', 'channels']), training=False)\n",
-        "    model_out *= [dataset.dataStats['std'][1], dataset.dataStats['std'][2]] # [u, v]\n",
-        "    correction =  to_phiflow(model_out, domain) \n",
-        "    source_vel_test = source_vel_test+correction\n",
-        "\n",
-        "    steps_hybrid.append( [source_dens_test, source_vel_test] )\n",
-        "    \n",
-        "print(\"Steps with hybrid solver \"+format(len(steps_hybrid)))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tnHYeOfldIxp"
-      },
-      "source": [
-        "Given the stored states, we quantify the improvements that the NN yields, and visualize the results. \n",
-        "\n",
-        "In the following cells, the index `b` chooses one of the four test simulations (by default index 0, the lowest Re outside the training data range), and computes the accumulated mean absolute error (MAE) over all time steps.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 316
-        },
-        "id": "bU-PwcCCdIxq",
-        "outputId": "932e00dd-b261-4eaf-d5ab-ca48729efc57"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "MAE for source: 0.13729144632816315 , and hybrid: 0.045980848371982574\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ],
-      "source": [
-        "import pylab\n",
-        "b = 0 # batch index for the following comparisons\n",
-        "\n",
-        "errors_source, errors_pred = [], []\n",
-        "for index in range(100):\n",
-        "  vx_ref = dataset_test.dataPreloaded[ dataset_test.dataSims[b] ][ index ][1][0,...]\n",
-        "  vy_ref = dataset_test.dataPreloaded[ dataset_test.dataSims[b] ][ index ][2][0,...]\n",
-        "  vxs = vx_ref - steps_source[index][1].values.vector[1].numpy('batch,y,x')[b,...]\n",
-        "  vxh = vx_ref - steps_hybrid[index][1].values.vector[1].numpy('batch,y,x')[b,...]\n",
-        "  vys = vy_ref - steps_source[index][1].values.vector[0].numpy('batch,y,x')[b,...] \n",
-        "  vyh = vy_ref - steps_hybrid[index][1].values.vector[0].numpy('batch,y,x')[b,...] \n",
-        "  errors_source.append(np.mean(np.abs(vxs)) + np.mean(np.abs(vys))) \n",
-        "  errors_pred.append(np.mean(np.abs(vxh)) + np.mean(np.abs(vyh)))\n",
-        "\n",
-        "fig = pylab.figure().gca()\n",
-        "pltx = np.linspace(0,99,100)\n",
-        "fig.plot(pltx, errors_source, lw=2, color='mediumblue', label='Source')  \n",
-        "fig.plot(pltx, errors_pred,   lw=2, color='green', label='Hybrid')\n",
-        "pylab.xlabel('Time step'); pylab.ylabel('Error'); fig.legend()\n",
-        "\n",
-        "print(\"MAE for source: \"+format(np.mean(errors_source)) +\" , and hybrid: \"+format(np.mean(errors_pred)) )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aOQP6iCBdIxs"
-      },
-      "source": [
-        "Due to the complexity of the training, the performance varies but typically the overall MAE is ca. 160% larger for the regular simulation compared to the hybrid simulator. \n",
-        "The gap is typically even bigger for other Reynolds numbers within the training data range. \n",
-        "The graph above also shows this behavior over time.\n",
-        "\n",
-        "Let's also visualize the differences of the two outputs by plotting the y component of the velocities over time. The two following code cells show six velocity snapshots for the batch index `b` in intervals of 20 time steps."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 310
-        },
-        "id": "_3f8uhIIdIxs",
-        "outputId": "ac76c9d2-1f79-4942-c9ea-1b45dfa810bf"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1152x360 with 6 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ],
-      "source": [
-        "c = 0          # channel selector, x=1 or y=0 \n",
-        "interval = 20  # time interval\n",
-        "\n",
-        "fig, axes = pylab.subplots(1, 6, figsize=(16, 5))    \n",
-        "for i in range(0,6):\n",
-        "  v = steps_source[i*interval][1].values.vector[c].numpy('batch,y,x')[b,...]\n",
-        "  axes[i].imshow( v , origin='lower', cmap='magma')\n",
-        "  axes[i].set_title(f\" Source simulation t={i*interval} \")\n",
-        "\n",
-        "pylab.tight_layout()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 321
-        },
-        "id": "v2d2WTGedIxt",
-        "outputId": "1e017623-3339-4c25-938c-8422659e8cc6"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1152x360 with 6 Axes>"
-            ],
-            "image/png": 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-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ],
-      "source": [
-        "fig, axes = pylab.subplots(1, 6, figsize=(16, 5))\n",
-        "for i in range(0,6):\n",
-        "  v = steps_hybrid[i*interval][1].values.vector[c].numpy('batch,y,x')[b,...]\n",
-        "  axes[i].imshow( v , origin='lower', cmap='magma')\n",
-        "  axes[i].set_title(f\" Hybrid solver t={i*interval} \")\n",
-        "pylab.tight_layout()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ivS0SUiYdIxt"
-      },
-      "source": [
-        "They both start out with the same initial state at $t=0$ (the downsampled solution from the reference solution manifold), and at $t=20$ the solutions still share similarities. Over time, the source version strongly diffuses the structures in the flow and looses momentum. The flow behind the obstacles becomes straight, and lacks clear vortices. \n",
-        "\n",
-        "The version produced by the hybrid solver does much better. It preserves the vortex shedding even after more than one hundred updates. Note that both outputs were produced by the same underlying solver. The second version just profits from the learned corrector which manages to revert the numerical errors of the source solver, including its overly strong dissipation. \n",
-        "\n",
-        "We also visually compare how the NN does w.r.t. reference data. The next cell plots one time step of the three versions: the reference data after 50 steps, and the re-simulated version of the source and our hybrid solver, together with a per-cell error of the two:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 358
-        },
-        "id": "23yyfljqdIxu",
-        "outputId": "0d9022a2-edc7-49ec-840c-c1a78762d0c8"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1008x360 with 4 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ],
-      "source": [
-        "index = 50 # time step index\n",
-        "vx_ref = dataset_test.dataPreloaded[ dataset_test.dataSims[b] ][ index ][1][0,...]\n",
-        "vx_src = steps_source[index][1].values.vector[1].numpy('batch,y,x')[b,...]\n",
-        "vx_hyb = steps_hybrid[index][1].values.vector[1].numpy('batch,y,x')[b,...]\n",
-        "\n",
-        "fig, axes = pylab.subplots(1, 4, figsize=(14, 5))\n",
-        "\n",
-        "axes[0].imshow( vx_ref , origin='lower', cmap='magma')\n",
-        "axes[0].set_title(f\" Reference \")\n",
-        "\n",
-        "axes[1].imshow( vx_src , origin='lower', cmap='magma')\n",
-        "axes[1].set_title(f\" Source \")\n",
-        "\n",
-        "axes[2].imshow( vx_hyb , origin='lower', cmap='magma')\n",
-        "axes[2].set_title(f\" Learned \")\n",
-        "\n",
-        "# show error side by side\n",
-        "err_source = vx_ref - vx_src \n",
-        "err_hybrid = vx_ref - vx_hyb \n",
-        "v = np.concatenate([err_source,err_hybrid], axis=1)\n",
-        "axes[3].imshow( v , origin='lower', cmap='cividis')\n",
-        "axes[3].set_title(f\" Errors: Source & Learned\")\n",
-        "\n",
-        "pylab.tight_layout()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BZByQsAydIxv"
-      },
-      "source": [
-        "This shows very clearly how the pure source simulation in the middle deviates from the reference on the left. The learned version stays much closer to the reference solution. \n",
-        "\n",
-        "The two per-cell error images on the right also illustrate this: the source version has much larger errors (i.e. brighter colors) that show how it systematically underestimates the vortices that should form. The error for the learned version is much more evenly distributed and significantly smaller in magnitude.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UQTY8m6LdIxv"
-      },
-      "source": [
-        "This concludes our evaluation. Note that the improved behavior of the hybrid solver can be difficult to reliably measure with simple vector norms such as an MAE or $L^2$ norm. To improve this, we'd need to employ other, domain-specific metrics. In this case, metrics for fluids based on vorticity and turbulence properties of the flow would be applicable. However, in this text, we instead want to focus on DL-related topics and target another inverse problem with differentiable physics solvers in the next chapter."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Dl3vzF_XdIxv"
-      },
-      "source": [
-        "## Next steps\n",
-        "\n",
-        "* Modify the training to further reduce the training error. With the _medium_ network you should be able to get the loss down to around 1.\n",
-        "\n",
-        "* Turn off the differentiable physics training (by setting `msteps=1`), and compare it with the DP version.\n",
-        "\n",
-        "* Likewise, train a network with a larger `msteps` setting, e.g., 8 or 16. Note that due to the recurrent nature of the training, you'll probably have to load a pre-trained state to stabilize the first iterations.\n",
-        "\n",
-        "* Use the external github code to generate new test data, and run your trained NN on these cases. You'll see that a reduced training error not always directly correlates with an improved test performance.\n",
-        "\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [],
-      "name": "diffphys-code-sol-jun8.ipynb",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.5"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qT_RWmTEugu9"
+   },
+   "source": [
+    "# Reducing Numerical Errors with Deep Learning\n",
+    "\n",
+    "In this example we will target numerical errors that arise in the discretization of a continuous PDE $\\mathcal P^*$, i.e. when we formulate $\\mathcal P$. This approach will demonstrate that, despite the lack of closed-form descriptions, discretization errors often are functions with regular and repeating structures and, thus, can be learned by a neural network. Once the network is trained, it can be evaluated locally to improve the solution of a PDE-solver, i.e., to reduce its numerical error. The resulting method is a hybrid one: it will always run (a coarse) PDE solver, and then improve it at runtime with corrections inferred by an NN.\n",
+    "\n",
+    " \n",
+    "Pretty much all numerical methods contain some form of iterative process: repeated updates over time for explicit solvers, or within a single update step for implicit solvers. \n",
+    "An example for the second case could be found [here](https://github.com/tum-pbs/CG-Solver-in-the-Loop),\n",
+    "but below we'll target the first case, i.e. iterations over time.\n",
+    "[[run in colab]](https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/diffphys-code-sol.ipynb)\n",
+    "\n",
+    "\n",
+    "## Problem formulation\n",
+    "\n",
+    "In the context of reducing errors, it's crucial to have a _differentiable physics solver_, so that the learning process can take the reaction of the solver into account. This interaction is not possible with supervised learning or PINN training. Even small inference errors of a supervised NN accumulate over time, and lead to a data distribution that differs from the distribution of the pre-computed data. This distribution shift leads to sub-optimal results, or even cause blow-ups of the solver.\n",
+    "\n",
+    "In order to learn the error function, we'll consider two different discretizations of the same PDE $\\mathcal P^*$: \n",
+    "a _reference_ version, which we assume to be accurate, with a discretized version \n",
+    "$\\mathcal P_r$, and solutions $\\mathbf r \\in \\mathscr R$, where $\\mathscr R$ denotes the manifold of solutions of $\\mathcal P_r$.\n",
+    "In parallel to this, we have a less accurate approximation of the same PDE, which we'll refer to as the _source_ version, as this will be the solver that our NN should later on interact with. Analogously,\n",
+    "we have $\\mathcal P_s$ with solutions $\\mathbf s \\in \\mathscr S$.\n",
+    "After training, we'll obtain a _hybrid_ solver that uses $\\mathcal P_s$ in conjunction with a trained network to obtain improved solutions, i.e., solutions that are closer to the ones produced by $\\mathcal P_r$.\n",
+    "\n",
+    "```{figure} resources/diffphys-sol-manifolds.jpeg\n",
+    "---\n",
+    "height: 150px\n",
+    "name: diffphys-sol-manifolds\n",
+    "---\n",
+    "Visual overview of coarse and reference manifolds\n",
+    "```\n"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tayrJa7_ZzS_"
+   },
+   "source": [
+    "\n",
+    "Let's assume $\\mathcal{P}$ advances a solution by a time step $\\Delta t$, and let's denote $n$ consecutive steps by a superscript:\n",
+    "$\n",
+    "\\newcommand{\\pde}{\\mathcal{P}}\n",
+    "\\newcommand{\\pdec}{\\pde_{s}}\n",
+    "\\newcommand{\\vc}[1]{\\mathbf{s}_{#1}} \n",
+    "\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \n",
+    "\\newcommand{\\vcN}{\\vs}          \n",
+    "\\newcommand{\\project}{\\mathcal{T}}   \n",
+    "\\pdec^n ( \\mathcal{T} \\vr{t} ) = \\pdec(\\pdec(\\cdots \\pdec( \\mathcal{T} \\vr{t}  )\\cdots)) .\n",
+    "$ \n",
+    "The corresponding state of the simulation is\n",
+    "$\n",
+    "\\mathbf{s}_{t+n} = \\mathcal{P}^n ( \\mathcal{T} \\mathbf{r}_{t} ) .\n",
+    "$\n",
+    "Here we assume a mapping operator $\\mathcal{T}$ exists that transfers a reference solution to the source manifold. This could, e.g., be a simple downsampling operation.\n",
+    "Especially for longer sequences, i.e. larger $n$, the source state \n",
+    "$\\newcommand{\\vc}[1]{\\mathbf{s}_{#1}} \\vc{t+n}$\n",
+    "will deviate from a corresponding reference state\n",
+    "$\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \\vr{t+n}$. \n",
+    "This is what we will address with an NN in the following.\n",
+    "\n",
+    "As before, we'll use an $L^2$-norm to quantify the deviations, i.e., \n",
+    "an error function $\\newcommand{\\loss}{e} \n",
+    "\\newcommand{\\corr}{\\mathcal{C}} \n",
+    "\\newcommand{\\vc}[1]{\\mathbf{s}_{#1}} \n",
+    "\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \n",
+    "\\loss (\\vc{t},\\mathcal{T} \\vr{t})=\\Vert\\vc{t}-\\mathcal{T} \\vr{t}\\Vert_2$. \n",
+    "Our learning goal is to train at a correction operator \n",
+    "$\\mathcal{C} ( \\mathbf{s} )$ such that \n",
+    "a solution to which the correction is applied has a lower error than the original unmodified (source) \n",
+    "solution: $\\newcommand{\\loss}{e} \n",
+    "\\newcommand{\\corr}{\\mathcal{C}} \n",
+    "\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \n",
+    "\\loss ( \\mathcal{P}_{s}( \\corr (\\mathcal{T} \\vr{t}) ) , \\mathcal{T} \\vr{t+1}) < \\loss ( \\mathcal{P}_{s}( \\mathcal{T} \\vr{t} ), \\mathcal{T} \\vr{t+1})$. \n",
+    "\n",
+    "The correction function \n",
+    "$\\newcommand{\\vcN}{\\mathbf{s}} \\newcommand{\\corr}{\\mathcal{C}} \\corr (\\vcN | \\theta)$ \n",
+    "is represented as a deep neural network with weights $\\theta$\n",
+    "and receives the state $\\mathbf{s}$ to infer an additive correction field with the same dimension.\n",
+    "To distinguish the original states $\\mathbf{s}$ from the corrected ones, we'll denote the latter with an added tilde $\\tilde{\\mathbf{s}}$.\n",
+    "The overall learning goal now becomes\n",
+    "\n",
+    "$$\n",
+    "\\newcommand{\\corr}{\\mathcal{C}}  \n",
+    "\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}} \n",
+    "\\text{arg min}_\\theta \\big( ( \\mathcal{P}_{s} \\corr )^n ( \\mathcal{T} \\vr{t} ) - \\mathcal{T} \\vr{t+n} \\big)^2\n",
+    "$$\n",
+    "\n",
+    "To simplify the notation, we've dropped the sum over different samples here (the $i$ from previous versions).\n",
+    "A crucial bit that's easy to overlook in the equation above, is that the correction depends on the modified states, i.e.\n",
+    "it is a function of\n",
+    "$\\tilde{\\mathbf{s}}$, so we have \n",
+    "$\\newcommand{\\vctN}{\\tilde{\\mathbf{s}}} \\newcommand{\\corr}{\\mathcal{C}} \\corr (\\vctN | \\theta)$.\n",
+    "These states actually evolve over time when training. They don't exist beforehand.\n",
+    "\n",
+    "**TL;DR**:\n",
+    "We'll train a network $\\mathcal{C}$ to reduce the numerical errors of a simulator with a more accurate reference. It's crucial to have the _source_ solver realized as a differential physics operator, such that it provides gradients for an improved training of $\\mathcal{C}$.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hPgwGkzYdIww"
+   },
+   "source": [
+    "## Getting started with the implementation\n",
+    "\n",
+    "The following replicates an experiment from [Solver-in-the-loop: learning from differentiable physics to interact with iterative pde-solvers](https://ge.in.tum.de/publications/2020-um-solver-in-the-loop/) {cite}`holl2019pdecontrol`, further details can be found in section B.1 of the [appendix](https://arxiv.org/pdf/2007.00016.pdf) of the paper.\n",
+    "\n",
+    "First, let's download the prepared data set (for details on generation & loading cf. https://github.com/tum-pbs/Solver-in-the-Loop), and let's get the data handling out of the way, so that we can focus on the _interesting_ parts..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JwZudtWauiGa",
+    "outputId": "f284b19b-0a77-44de-befe-06e3218fe49d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading training data (73MB), this can take a moment the first time...\n",
+      "Loaded data, 6 training sims\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, sys, logging, argparse, pickle, glob, random, distutils.dir_util, urllib.request\n",
+    "\n",
+    "fname_train = 'sol-karman-2d-train.pickle'\n",
+    "if not os.path.isfile(fname_train):\n",
+    "  print(\"Downloading training data (73MB), this can take a moment the first time...\")\n",
+    "  urllib.request.urlretrieve(\"https://physicsbaseddeeplearning.org/data/\"+fname_train, fname_train)\n",
+    "\n",
+    "with open(fname_train, 'rb') as f: data_preloaded = pickle.load(f)\n",
+    "print(\"Loaded data, {} training sims\".format(len(data_preloaded)) )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RY1F4kdWPLNG"
+   },
+   "source": [
+    "Also let's get installing / importing all the necessary libraries out of the way. And while we're at it, we set the random seed - obviously, 42 is the ultimate choice here 🙂"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "BGN4GqxkIueM",
+    "outputId": "53dafe3f-e5b1-49d4-f9c0-8c2551e5f84f"
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install --upgrade --quiet phiflow==2.2\n",
+    "from phi.tf.flow import *\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "random.seed(42) \n",
+    "np.random.seed(42)\n",
+    "tf.random.set_seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OhnzPdoww11P"
+   },
+   "source": [
+    "## Simulation setup\n",
+    "\n",
+    "Now we set up the _source_ simulation $\\mathcal{P}_{s}$. \n",
+    "Note that we won't deal with \n",
+    "$\\mathcal{P}_{r}$\n",
+    "below: the downsampled reference data is contained in the training data set. It was generated with a four times finer discretization. Below we're focusing on the interaction of the source solver and the NN. \n",
+    "\n",
+    "This code block and the next ones will define lots of functions, that will be used later on for training.\n",
+    "\n",
+    "The `KarmanFlow` solver below simulates a relatively standard wake flow case with a spherical obstacle in a rectangular domain, and an explicit viscosity solve to obtain different Reynolds numbers. This is the geometry of the setup:\n",
+    "\n",
+    "```{figure} resources/diffphys-sol-domain.png\n",
+    "---\n",
+    "height: 200px\n",
+    "name: diffphys-sol-domain\n",
+    "---\n",
+    "Domain setup for the wake flow case (sizes in the imlpementation are using an additional factor of 100).\n",
+    "```\n",
+    "\n",
+    "The solver applies inflow boundary conditions for the y-velocity with a pre-multiplied mask (`vel_BcMask`), to set the y components at the bottom of the domain during the simulation step. This mask is created with the `HardGeometryMask` from phiflow, which initializes the spatially shifted entries for the components of a staggered grid correctly. The simulation step is quite straight forward: it computes contributions for viscosity, inflow, advection and finally makes the resulting motion divergence free via an implicit pressure solve:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "6WNMcdWUw4EP"
+   },
+   "outputs": [],
+   "source": [
+    "class KarmanFlow():\n",
+    "    def __init__(self, domain):\n",
+    "        self.domain = domain\n",
+    "\n",
+    "        self.vel_BcMask = self.domain.staggered_grid(HardGeometryMask( Box(y=(None, 5), x=None) ) )\n",
+    "    \n",
+    "        self.inflow = self.domain.scalar_grid(Box(y=(25,75), x=(5,10)) ) # scale with domain if necessary!\n",
+    "        self.obstacles = [Obstacle(Sphere(center=tensor([50, 50], channel(vector=\"y,x\")), radius=10))] \n",
+    "        #\n",
+    "\n",
+    "    def step(self, density_in, velocity_in, re, res, buoyancy_factor=0, dt=1.0):\n",
+    "        velocity = velocity_in\n",
+    "        density = density_in\n",
+    "\n",
+    "        # viscosity\n",
+    "        velocity = phi.flow.diffuse.explicit(field=velocity, diffusivity=1.0/re*dt*res*res, dt=dt)\n",
+    "        \n",
+    "        # inflow boundary conditions\n",
+    "        velocity = velocity*(1.0 - self.vel_BcMask) + self.vel_BcMask * (1,0)\n",
+    "\n",
+    "        # advection \n",
+    "        density = advect.semi_lagrangian(density+self.inflow, velocity, dt=dt)\n",
+    "        velocity = advected_velocity = advect.semi_lagrangian(velocity, velocity, dt=dt)\n",
+    "\n",
+    "        # mass conservation (pressure solve)\n",
+    "        pressure = None\n",
+    "        velocity, pressure = fluid.make_incompressible(velocity, self.obstacles)\n",
+    "        self.solve_info = { 'pressure': pressure, 'advected_velocity': advected_velocity }\n",
+    "        \n",
+    "        return [density, velocity]\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RYFUGICgxk0K"
+   },
+   "source": [
+    "## Network architecture\n",
+    "\n",
+    "We'll also define two alternative versions of a neural networks to represent \n",
+    "$\\newcommand{\\vcN}{\\mathbf{s}} \\newcommand{\\corr}{\\mathcal{C}} \\corr$. In both cases we'll use fully convolutional networks, i.e. networks without any fully-connected layers. We'll use Keras within tensorflow to define the layers of the network (mostly via `Conv2D`), typically activated via ReLU and LeakyReLU functions, respectively.\n",
+    "The inputs to the network are: \n",
+    "- 2 fields with x,y velocity\n",
+    "- the Reynolds number as constant channel.\n",
+    "\n",
+    "The output is: \n",
+    "- a 2 component field containing the x,y velocity.\n",
+    "\n",
+    "First, let's define a small network consisting only of four convolutional layers with ReLU activations (we're also using keras here for simplicity). The input dimensions are determined from input tensor in the `inputs_dict` (it has three channels: u,v, and Re). Then we process the data via three conv layers with 32 features each, before reducing to 2 channels in the output. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "qIrWYTy6xscA"
+   },
+   "outputs": [],
+   "source": [
+    "def network_small(inputs_dict):\n",
+    "    l_input = keras.layers.Input(**inputs_dict)\n",
+    "    block_0 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_input)\n",
+    "    block_0 = keras.layers.LeakyReLU()(block_0)\n",
+    "\n",
+    "    l_conv1 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_0)\n",
+    "    l_conv1 = keras.layers.LeakyReLU()(l_conv1)\n",
+    "    l_conv2 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv1)\n",
+    "    block_1 = keras.layers.LeakyReLU()(l_conv2)\n",
+    "\n",
+    "    l_output = keras.layers.Conv2D(filters=2,  kernel_size=5, padding='same')(block_1) # u, v\n",
+    "    return keras.models.Model(inputs=l_input, outputs=l_output)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YfHvdI7yxtdj"
+   },
+   "source": [
+    "For flexibility (and larger-scale tests later on), let's also define a _proper_ ResNet with a few more layers. This architecture is the one from the original paper, and will give a fairly good performance (`network_small` above will train faster, but give a sub-optimal performance at inference time)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "TyfpA7Fbx0ro"
+   },
+   "outputs": [],
+   "source": [
+    "def network_medium(inputs_dict):\n",
+    "    l_input = keras.layers.Input(**inputs_dict)\n",
+    "    block_0 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_input)\n",
+    "    block_0 = keras.layers.LeakyReLU()(block_0)\n",
+    "\n",
+    "    l_conv1 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_0)\n",
+    "    l_conv1 = keras.layers.LeakyReLU()(l_conv1)\n",
+    "    l_conv2 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv1)\n",
+    "    l_skip1 = keras.layers.add([block_0, l_conv2])\n",
+    "    block_1 = keras.layers.LeakyReLU()(l_skip1)\n",
+    "\n",
+    "    l_conv3 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_1)\n",
+    "    l_conv3 = keras.layers.LeakyReLU()(l_conv3)\n",
+    "    l_conv4 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv3)\n",
+    "    l_skip2 = keras.layers.add([block_1, l_conv4])\n",
+    "    block_2 = keras.layers.LeakyReLU()(l_skip2)\n",
+    "\n",
+    "    l_conv5 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_2)\n",
+    "    l_conv5 = keras.layers.LeakyReLU()(l_conv5)\n",
+    "    l_conv6 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv5)\n",
+    "    l_skip3 = keras.layers.add([block_2, l_conv6])\n",
+    "    block_3 = keras.layers.LeakyReLU()(l_skip3)\n",
+    "\n",
+    "    l_conv7 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_3)\n",
+    "    l_conv7 = keras.layers.LeakyReLU()(l_conv7)\n",
+    "    l_conv8 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv7)\n",
+    "    l_skip4 = keras.layers.add([block_3, l_conv8])\n",
+    "    block_4 = keras.layers.LeakyReLU()(l_skip4)\n",
+    "\n",
+    "    l_conv9 = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(block_4)\n",
+    "    l_conv9 = keras.layers.LeakyReLU()(l_conv9)\n",
+    "    l_convA = keras.layers.Conv2D(filters=32, kernel_size=5, padding='same')(l_conv9)\n",
+    "    l_skip5 = keras.layers.add([block_4, l_convA])\n",
+    "    block_5 = keras.layers.LeakyReLU()(l_skip5)\n",
+    "\n",
+    "    l_output = keras.layers.Conv2D(filters=2,  kernel_size=5, padding='same')(block_5)\n",
+    "    return keras.models.Model(inputs=l_input, outputs=l_output)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ew-MgPSlyLW-"
+   },
+   "source": [
+    "Next, we're coming to two functions which are pretty important: they transform the simulation state into an input tensor for the network, and vice versa. Hence, they're the interface between _keras/tensorflow_ and _phiflow_.\n",
+    "\n",
+    "The `to_keras` function uses the two vector components via `vector['x']` and `vector['y']` to discard the outermost layer of the velocity field grids. This gives two tensors of equal size that are concatenated. \n",
+    "It then adds a constant channel via `math.ones` that is multiplied by the desired Reynolds number in `ext_const_channel`. The resulting stack of grids is stacked along the `channels` dimensions, and represents an  input to the neural network. \n",
+    "\n",
+    "After network evaluation, we transform the output tensor back into a phiflow grid via the `to_phiflow` function. \n",
+    "It converts the 2-component tensor that is returned by the network into a phiflow staggered grid object, so that it is compatible with the velocity field of the fluid simulation.\n",
+    "(Note: these are two _centered_ grids with different sizes, so we leave the work to the `domain.staggered_grid` function, which also sets physical size and boundary conditions as given by the domain)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "hhGFpTjGyRyg"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def to_keras(dens_vel_grid_array, ext_const_channel):\n",
+    "    # align the sides the staggered velocity grid making its size the same as the centered grid\n",
+    "    return math.stack(\n",
+    "        [\n",
+    "            math.pad( dens_vel_grid_array[1].vector['x'].values, {'x':(0,1)} , math.extrapolation.ZERO),\n",
+    "            dens_vel_grid_array[1].vector['y'].y[:-1].values,         # v\n",
+    "            math.ones(dens_vel_grid_array[0].shape)*ext_const_channel # Re\n",
+    "        ],\n",
+    "        math.channel('channels')\n",
+    "    )\n",
+    "\n",
+    "def to_phiflow(tf_tensor, domain):\n",
+    "    return domain.staggered_grid(\n",
+    "        math.stack(\n",
+    "            [\n",
+    "                math.tensor(tf.pad(tf_tensor[..., 1], [(0,0), (0,1), (0,0)]), math.batch('batch'), math.spatial('y, x')), # v\n",
+    "                math.tensor( tf_tensor[...,:-1, 0], math.batch('batch'), math.spatial('y, x')), # u \n",
+    "            ], math.channel(vector=\"y,x\")\n",
+    "        ) \n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VngMwN_9y00S"
+   },
+   "source": [
+    "---\n",
+    "\n",
+    "## Data handling\n",
+    "\n",
+    "So far so good - we also need to take care of a few more mundane tasks, e.g., some data handling and randomization. Below we define a `Dataset` class that stores all \"ground truth\" reference data (already downsampled).\n",
+    "\n",
+    "We actually have a lot of data dimensions: multiple simulations, with many time steps, each with different fields. This makes the code below a bit more difficult to read.\n",
+    "\n",
+    "The data format for the numpy array `dataPreloaded`: is  `['sim_name', frame, field (dens & vel)]`, where each field has dimension `[batch-size, y-size, x-size, channels]` (this is the standard for a phiflow export)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "tjywcdD2y20t"
+   },
+   "outputs": [],
+   "source": [
+    "class Dataset():\n",
+    "    def __init__(self, data_preloaded, num_frames, num_sims=None, batch_size=1, is_testset=False):\n",
+    "        self.epoch         = None\n",
+    "        self.epochIdx      = 0\n",
+    "        self.batch         = None\n",
+    "        self.batchIdx      = 0\n",
+    "        self.step          = None\n",
+    "        self.stepIdx       = 0\n",
+    "\n",
+    "        self.dataPreloaded = data_preloaded\n",
+    "        self.batchSize     = batch_size\n",
+    "\n",
+    "        self.numSims       = num_sims\n",
+    "        self.numBatches    = num_sims//batch_size\n",
+    "        self.numFrames     = num_frames\n",
+    "        self.numSteps      = num_frames\n",
+    "        \n",
+    "        # initialize directory keys (using naming scheme from SoL codebase)\n",
+    "        # constant additional per-sim channel: Reynolds numbers from data generation\n",
+    "        # hard coded for training and test data here\n",
+    "        if not is_testset:\n",
+    "            self.dataSims = ['karman-fdt-hires-set/sim_%06d'%i for i in range(num_sims) ]\n",
+    "            ReNrs = [160000.0, 320000.0, 640000.0,  1280000.0,  2560000.0,  5120000.0]\n",
+    "            self.extConstChannelPerSim = { self.dataSims[i]:[ReNrs[i]] for i in range(num_sims) }\n",
+    "        else:\n",
+    "            self.dataSims = ['karman-fdt-hires-testset/sim_%06d'%i for i in range(num_sims) ]\n",
+    "            ReNrs = [120000.0, 480000.0, 1920000.0, 7680000.0] \n",
+    "            self.extConstChannelPerSim = { self.dataSims[i]:[ReNrs[i]] for i in range(num_sims) }\n",
+    "\n",
+    "        self.dataFrames = [ np.arange(num_frames) for _ in self.dataSims ]  \n",
+    "\n",
+    "        # debugging example, check shape of a single marker density field:\n",
+    "        #print(format(self.dataPreloaded[self.dataSims[0]][0][0].shape )) \n",
+    "        \n",
+    "        # the data has the following shape ['sim', frame, field (dens/vel)] where each field is [batch-size, y-size, x-size, channels]\n",
+    "        self.resolution = self.dataPreloaded[self.dataSims[0]][0][0].shape[1:3]  \n",
+    "\n",
+    "        # compute data statistics for normalization\n",
+    "        self.dataStats = {\n",
+    "            'std': (\n",
+    "                np.std(np.concatenate([np.absolute(self.dataPreloaded[asim][i][0].reshape(-1)) for asim in self.dataSims for i in range(num_frames)], axis=-1)), # density\n",
+    "                np.std(np.concatenate([np.absolute(self.dataPreloaded[asim][i][1].reshape(-1)) for asim in self.dataSims for i in range(num_frames)], axis=-1)), # x-velocity\n",
+    "                np.std(np.concatenate([np.absolute(self.dataPreloaded[asim][i][2].reshape(-1)) for asim in self.dataSims for i in range(num_frames)], axis=-1)), # y-velocity\n",
+    "            )\n",
+    "        }\n",
+    "        self.dataStats.update({\n",
+    "            'ext.std': [ np.std([np.absolute(self.extConstChannelPerSim[asim][0]) for asim in self.dataSims]) ] # Reynolds Nr\n",
+    "        })\n",
+    "\n",
+    "        \n",
+    "        if not is_testset:\n",
+    "            print(\"Data stats: \"+format(self.dataStats))\n",
+    "\n",
+    "\n",
+    "    # re-shuffle data for next epoch\n",
+    "    def newEpoch(self, exclude_tail=0, shuffle_data=True):\n",
+    "        self.numSteps = self.numFrames - exclude_tail\n",
+    "        simSteps = [ (asim, self.dataFrames[i][0:(len(self.dataFrames[i])-exclude_tail)]) for i,asim in enumerate(self.dataSims) ]\n",
+    "        sim_step_pair = []\n",
+    "        for i,_ in enumerate(simSteps):\n",
+    "            sim_step_pair += [ (i, astep) for astep in simSteps[i][1] ]  # (sim_idx, step) ...\n",
+    "\n",
+    "        if shuffle_data: random.shuffle(sim_step_pair)\n",
+    "        self.epoch = [ list(sim_step_pair[i*self.numSteps:(i+1)*self.numSteps]) for i in range(self.batchSize*self.numBatches) ]\n",
+    "        self.epochIdx += 1\n",
+    "        self.batchIdx = 0\n",
+    "        self.stepIdx = 0\n",
+    "\n",
+    "    def nextBatch(self):  \n",
+    "        self.batchIdx += self.batchSize\n",
+    "        self.stepIdx = 0\n",
+    "\n",
+    "    def nextStep(self):\n",
+    "        self.stepIdx += 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "twIMJ3V0N1FX"
+   },
+   "source": [
+    "The `nextEpoch`, `nextBatch`, and `nextStep` functions will be called at training time to randomize the order of the training data.\n",
+    "\n",
+    "Now we need one more function that compiles the data for a mini batch to train with, called `getData` below. It returns batches of the desired size in terms of marker density, velocity, and Reynolds number.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "Dfwd4TnqN1Tn"
+   },
+   "outputs": [],
+   "source": [
+    "# for class Dataset():\n",
+    "def getData(self, consecutive_frames):\n",
+    "    d_hi = [\n",
+    "        np.concatenate([\n",
+    "            self.dataPreloaded[\n",
+    "                self.dataSims[self.epoch[self.batchIdx+i][self.stepIdx][0]] # sim_key\n",
+    "            ][\n",
+    "                self.epoch[self.batchIdx+i][self.stepIdx][1]+j # frames\n",
+    "            ][0]\n",
+    "            for i in range(self.batchSize)\n",
+    "        ], axis=0) for j in range(consecutive_frames+1)\n",
+    "    ]\n",
+    "    u_hi = [\n",
+    "        np.concatenate([\n",
+    "            self.dataPreloaded[\n",
+    "                self.dataSims[self.epoch[self.batchIdx+i][self.stepIdx][0]] # sim_key\n",
+    "            ][\n",
+    "                self.epoch[self.batchIdx+i][self.stepIdx][1]+j # frames\n",
+    "            ][1]\n",
+    "            for i in range(self.batchSize)\n",
+    "        ], axis=0) for j in range(consecutive_frames+1)\n",
+    "    ]\n",
+    "    v_hi = [\n",
+    "        np.concatenate([\n",
+    "            self.dataPreloaded[\n",
+    "                self.dataSims[self.epoch[self.batchIdx+i][self.stepIdx][0]] # sim_key\n",
+    "            ][\n",
+    "                self.epoch[self.batchIdx+i][self.stepIdx][1]+j # frames\n",
+    "            ][2]\n",
+    "            for i in range(self.batchSize)\n",
+    "        ], axis=0) for j in range(consecutive_frames+1)\n",
+    "    ]\n",
+    "    ext = [\n",
+    "        self.extConstChannelPerSim[\n",
+    "            self.dataSims[self.epoch[self.batchIdx+i][self.stepIdx][0]]\n",
+    "        ][0] for i in range(self.batchSize)\n",
+    "    ]\n",
+    "    return [d_hi, u_hi, v_hi, ext]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bIWnyPYlz8q7"
+   },
+   "source": [
+    "Note that the `density` here denotes a passively advected marker field, and not the density of the fluid. Below we'll be focusing on the velocity only, the marker density is tracked purely for visualization purposes.\n",
+    "\n",
+    "After all the definitions we can finally run some code. We define the dataset object with the downloaded data from the first cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "59EBdEdj0QR2",
+    "outputId": "c6f13916-2764-45dc-89f1-22890cb2662b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data stats: {'std': (2.6542656, 0.23155601, 0.3066732), 'ext.std': [1732512.6262166172]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "nsims = 6\n",
+    "batch_size = 3\n",
+    "simsteps = 500\n",
+    "\n",
+    "dataset = Dataset( data_preloaded=data_preloaded, num_frames=simsteps, num_sims=nsims, batch_size=batch_size )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0N92RooWPzeA"
+   },
+   "source": [
+    "Additionally, we've defined several global variables to control the training and the simulation in the next code cells.\n",
+    "\n",
+    "The most important and interesting one is `msteps`. It defines the number of simulation steps that are unrolled at each training iteration. This directly influences the runtime of each training step, as we first have to simulate all steps forward, and then backpropagate the gradient through all `msteps` simulation steps interleaved with the NN evaluations. However, this is where we'll receive important feedback in terms of gradients how the inferred corrections actually influence a running simulation. Hence, larger `msteps` are typically better.\n",
+    "\n",
+    "In addition we define the resolution of the simulation in `source_res`, and allocate the fluid solver object called `simulator`. In order to create grids, it requires access to a `Domain` object, which mostly exists for convenience purposes: it stores resolution, physical size in `bounds`, and boundary conditions of the domain. This information needs to be passed to every grid, and hence it's convenient to have it in one place in the form of the `Domain`. For the setup described above, we need different boundary conditions along x and y: closed walls, and free flow in and out of the domain, respecitvely.\n",
+    "\n",
+    "We also instantiate the actual NN `network` in the next cell. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "EjgkdCzKP2Ip",
+    "outputId": "9e95aa79-6124-4e30-baf1-28bc37801b0f"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-10-594cd5e5046a>:10: FutureWarning: Domain (phi.physics._boundaries) is deprecated and will be removed in a future release.\n",
+      "Please create grids directly, replacing the domain with a dict, e.g.\n",
+      "    domain = dict(x=64, y=128, bounds=Box(x=1, y=1))\n",
+      "    grid = CenteredGrid(0, **domain)\n",
+      "  from phi.physics._boundaries import Domain, OPEN, STICKY as CLOSED\n",
+      "<ipython-input-10-594cd5e5046a>:17: DeprecationWarning: Domain is deprecated and will be removed in a future release. Use a dict instead, e.g. CenteredGrid(values, extrapolation, **domain_dict)\n",
+      "  domain = phi.physics._boundaries.Domain(y=source_res[0], x=source_res[1], boundaries=boundary_conditions, bounds=Box(y=2*simulation_length, x=simulation_length))\n",
+      "<ipython-input-10-594cd5e5046a>:17: FutureWarning: Domain is deprecated and will be removed in a future release. Use a dict instead, e.g. CenteredGrid(values, extrapolation, **domain_dict)\n",
+      "  domain = phi.physics._boundaries.Domain(y=source_res[0], x=source_res[1], boundaries=boundary_conditions, bounds=Box(y=2*simulation_length, x=simulation_length))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_1 (InputLayer)        [(None, 64, 32, 3)]       0         \n",
+      "                                                                 \n",
+      " conv2d (Conv2D)             (None, 64, 32, 32)        2432      \n",
+      "                                                                 \n",
+      " leaky_re_lu (LeakyReLU)     (None, 64, 32, 32)        0         \n",
+      "                                                                 \n",
+      " conv2d_1 (Conv2D)           (None, 64, 32, 32)        25632     \n",
+      "                                                                 \n",
+      " leaky_re_lu_1 (LeakyReLU)   (None, 64, 32, 32)        0         \n",
+      "                                                                 \n",
+      " conv2d_2 (Conv2D)           (None, 64, 32, 32)        25632     \n",
+      "                                                                 \n",
+      " leaky_re_lu_2 (LeakyReLU)   (None, 64, 32, 32)        0         \n",
+      "                                                                 \n",
+      " conv2d_3 (Conv2D)           (None, 64, 32, 2)         1602      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 55,298\n",
+      "Trainable params: 55,298\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# one of the most crucial parameters: how many simulation steps to look into the future while training\n",
+    "msteps = 4\n",
+    "\n",
+    "# # this is the actual resolution in terms of cells\n",
+    "source_res = list(dataset.resolution)\n",
+    "# # this is a virtual size, in terms of abstract units for the bounding box of the domain (it's important for conversions or when rescaling to physical units)\n",
+    "simulation_length = 100.\n",
+    "\n",
+    "# for readability\n",
+    "from phi.physics._boundaries import Domain, OPEN, STICKY as CLOSED\n",
+    "\n",
+    "boundary_conditions = {\n",
+    "    'y':(phi.physics._boundaries.OPEN,  phi.physics._boundaries.OPEN) ,\n",
+    "    'x':(phi.physics._boundaries.STICKY,phi.physics._boundaries.STICKY) \n",
+    "}\n",
+    "\n",
+    "domain = phi.physics._boundaries.Domain(y=source_res[0], x=source_res[1], boundaries=boundary_conditions, bounds=Box(y=2*simulation_length, x=simulation_length))\n",
+    "simulator = KarmanFlow(domain=domain)\n",
+    "\n",
+    "network = network_small(dict(shape=(source_res[0],source_res[1], 3)))\n",
+    "network.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AbpNPzplQZMF"
+   },
+   "source": [
+    "## Interleaving simulation and NN\n",
+    "\n",
+    "Now comes the **most crucial** step in the whole setup: we define a function that encapsulates the chain of simulation steps and network evaluations in each training step. After all the work defining helper functions, it's actually pretty simple: we create a gradient tape via `tf.GradientTape()` such that we can backpropagate later on. We then loop over `msteps`, call the simulator via `simulator.step` for an input state, and afterwards evaluate the correction via `network(to_keras(...))`. The NN correction is then added to the last simulation state in the `prediction` list (we're actually simply overwriting the last simulated velocity `prediction[-1][1]` with `prediction[-1][1] + correction[-1]`.\n",
+    "\n",
+    "One other important thing that's happening here is normalization: the inputs to the network are divided by the standard deviations in `dataset.dataStats`. After evaluating the `network`, we only have a velocity left, so we simply multiply it by the standard deviation of the velocity again (via `* dataset.dataStats['std'][1]` and `[2]`).\n",
+    "\n",
+    "The `training_step` function also directly evaluates and returns the loss. Here, we simply use an $L^2$ loss over the whole sequence, i.e. the iteration over `msteps`. This is requiring a few lines of code because we separately loop over 'x' and 'y' components, in order to normalize and compare to the ground truth values from the training data set.\n",
+    "\n",
+    "The \"learning\" happens in the last two lines via `tape.gradient()` and `opt.apply_gradients()`, which then contain the aggregated information about how to change the NN weights to nudge the simulation closer to the reference for the full chain of simulation steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "D5NeMcLGQaxh",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def training_step(dens_gt, vel_gt, Re, i_step):\n",
+    "    with tf.GradientTape() as tape:\n",
+    "        prediction, correction = [ [dens_gt[0],vel_gt[0]] ], [0] # predicted states with correction, inferred velocity corrections\n",
+    "\n",
+    "        for i in range(msteps):\n",
+    "            prediction += [\n",
+    "                simulator.step(\n",
+    "                    density_in=prediction[-1][0],\n",
+    "                    velocity_in=prediction[-1][1],\n",
+    "                    re=Re, res=source_res[1],\n",
+    "                )\n",
+    "            ]       # prediction: [[density1, velocity1], [density2, velocity2], ...]\n",
+    "\n",
+    "            model_input = to_keras(prediction[-1], Re)\n",
+    "            model_input /= math.tensor([dataset.dataStats['std'][1], dataset.dataStats['std'][2], dataset.dataStats['ext.std'][0]], channel('channels')) # [u, v, Re]\n",
+    "            model_out = network(model_input.native(['batch', 'y', 'x', 'channels']), training=True)\n",
+    "            model_out *= [dataset.dataStats['std'][1], dataset.dataStats['std'][2]] # [u, v]\n",
+    "            correction += [ to_phiflow(model_out, domain) ]                         # [velocity_correction1, velocity_correction2, ...]\n",
+    "\n",
+    "            prediction[-1][1] = prediction[-1][1] + correction[-1]\n",
+    " \n",
+    "        # evaluate loss\n",
+    "        loss_steps_x = [\n",
+    "            tf.nn.l2_loss(\n",
+    "                (\n",
+    "                    vel_gt[i].vector['x'].values.native(('batch', 'y', 'x'))\n",
+    "                    - prediction[i][1].vector['x'].values.native(('batch', 'y', 'x'))\n",
+    "                )/dataset.dataStats['std'][1]\n",
+    "            )\n",
+    "            for i in range(1,msteps+1)\n",
+    "        ]\n",
+    "        loss_steps_x_sum = tf.math.reduce_sum(loss_steps_x)\n",
+    "\n",
+    "        loss_steps_y = [\n",
+    "            tf.nn.l2_loss(\n",
+    "                (\n",
+    "                    vel_gt[i].vector['y'].values.native(('batch', 'y', 'x'))\n",
+    "                    - prediction[i][1].vector['y'].values.native(('batch', 'y', 'x'))\n",
+    "                )/dataset.dataStats['std'][2]\n",
+    "            )\n",
+    "            for i in range(1,msteps+1)\n",
+    "        ]\n",
+    "        loss_steps_y_sum = tf.math.reduce_sum(loss_steps_y)\n",
+    "\n",
+    "        loss = (loss_steps_x_sum + loss_steps_y_sum)/msteps\n",
+    "\n",
+    "        gradients = tape.gradient(loss, network.trainable_variables)\n",
+    "        opt.apply_gradients(zip(gradients, network.trainable_variables))\n",
+    "\n",
+    "        return math.tensor(loss)    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "c4yLlDM3QfUR"
+   },
+   "source": [
+    "Once defined, we prepare this function for executing the training step by calling phiflow's `math.jit_compile()` function. It automatically maps to the correct pre-compilation step of the chosen backend. E.g., for TF this internally creates a computational graph, and optimizes the chain of operations. For JAX, it can even compile optimized GPU code (if JAX is set up correctly). Thus, using the jit compilation can make a huge difference in terms of runtime."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "K2JcO3-QQgC9"
+   },
+   "outputs": [],
+   "source": [
+    "training_step_jit = math.jit_compile(training_step)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E6Vly1_0QhZ1"
+   },
+   "source": [
+    "## Training\n",
+    "\n",
+    "For the training, we use a standard Adam optimizer, and run 15 epochs by default. This should be increased for the larger network or to obtain more accurate results. For longer training runs, it would also be beneficial to decrease the learning rate over the course of the epochs, but for simplicity, we'll keep `LR` constant here.\n",
+    "\n",
+    "Optionally, this is also the right point to load a network state to resume training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "PuljFamYQksW"
+   },
+   "outputs": [],
+   "source": [
+    "LR = 1e-4\n",
+    "EPOCHS = 15\n",
+    "\n",
+    "opt = tf.keras.optimizers.Adam(learning_rate=LR) \n",
+    "\n",
+    "# optional, load existing network...\n",
+    "# set to epoch nr. to load existing network from there\n",
+    "resume = 0\n",
+    "if resume>0: \n",
+    "    ld_network = keras.models.load_model('./nn_epoch{:04d}.h5'.format(resume)) \n",
+    "    #ld_network = keras.models.load_model('./nn_final.h5') # or the last one\n",
+    "    network.set_weights(ld_network.get_weights())\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lrALctV1RWBO"
+   },
+   "source": [
+    "Finally, we can start training the NN! This is very straight forward now, we simply loop over the desired number of iterations, get a batch each time via `getData`, feed it into the source simulation input `source_in`, and compare it in the loss with the `reference` data for the batch.\n",
+    "\n",
+    "The setup above will automatically take care that the differentiable physics solver used here provides the right gradient information, and provides it to the tensorflow network. Be warned: due to the complexity of the setup, this training run can take a while... (If you have a saved `nn_final.h5` network from a previous run, you can potentially skip this block and load the previously trained model instead via the cell above.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "m3Nd8YyHRVFQ",
+    "outputId": "486715ab-73dd-4301-b5f3-db34b1d9c3c4",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 001/005, batch 001/002, step 0001/0496: loss=\u001b[94m2565.1914\u001b[0m\n",
+      "epoch 001/005, batch 001/002, step 0002/0496: loss=\u001b[94m1434.6736\u001b[0m\n",
+      "epoch 001/005, batch 001/002, step 0003/0496: loss=\u001b[94m724.7997\u001b[0m\n",
+      "epoch 001/005, batch 001/002, step 0129/0496: loss=\u001b[94m40.198242\u001b[0m\n",
+      "epoch 001/005, batch 001/002, step 0257/0496: loss=\u001b[94m28.450535\u001b[0m\n",
+      "epoch 001/005, batch 001/002, step 0385/0496: loss=\u001b[94m27.100056\u001b[0m\n",
+      "epoch 002/005, batch 001/002, step 0401/0496: loss=\u001b[94m8.376183\u001b[0m\n",
+      "epoch 003/005, batch 001/002, step 0401/0496: loss=\u001b[94m4.7433133\u001b[0m\n",
+      "epoch 004/005, batch 001/002, step 0401/0496: loss=\u001b[94m4.522671\u001b[0m\n",
+      "epoch 005/005, batch 001/002, step 0401/0496: loss=\u001b[94m2.0179803\u001b[0m\n",
+      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
+      "Training done, saved NN\n"
+     ]
+    }
+   ],
+   "source": [
+    "steps = 0\n",
+    "EPOCHS=5 # NT_DEBUG\n",
+    "for j in range(EPOCHS):  # training\n",
+    "    dataset.newEpoch(exclude_tail=msteps)\n",
+    "    if j<resume:\n",
+    "        print('resume: skipping {} epoch'.format(j+1))\n",
+    "        steps += dataset.numSteps*dataset.numBatches\n",
+    "        continue\n",
+    "\n",
+    "    for ib in range(dataset.numBatches):   \n",
+    "        for i in range(dataset.numSteps): \n",
+    "\n",
+    "            # batch: [[dens0, dens1, ...], [x-velo0, x-velo1, ...], [y-velo0, y-velo1, ...], [ReynoldsNr(s)]]            \n",
+    "            batch = getData(dataset, consecutive_frames=msteps)\n",
+    "            \n",
+    "            dens_gt = [   # [density0:CenteredGrid, density1, ...]\n",
+    "                domain.scalar_grid(\n",
+    "                    math.tensor(batch[0][k], math.batch('batch'), math.spatial('y, x')) \n",
+    "                ) for k in range(msteps+1)\n",
+    "            ]\n",
+    "\n",
+    "            vel_gt = [   # [velocity0:StaggeredGrid, velocity1, ...]\n",
+    "                domain.staggered_grid(\n",
+    "                    math.stack(\n",
+    "                        [\n",
+    "                            math.tensor(batch[2][k], math.batch('batch'), math.spatial('y, x')),\n",
+    "                            math.tensor(batch[1][k], math.batch('batch'), math.spatial('y, x')),\n",
+    "                        ], math.channel(vector=\"y,x\")\n",
+    "                    ) \n",
+    "                ) for k in range(msteps+1)\n",
+    "            ]\n",
+    "            re_nr = math.tensor(batch[3], math.batch('batch'))\n",
+    "\n",
+    "            loss = training_step_jit(dens_gt, vel_gt, re_nr, math.tensor(steps)) \n",
+    "            \n",
+    "            steps += 1\n",
+    "            if (j==0 and ib==0 and i<3) or (j==0 and ib==0 and i%128==0) or (j>0 and ib==0 and i==400): # reduce output \n",
+    "              print('epoch {:03d}/{:03d}, batch {:03d}/{:03d}, step {:04d}/{:04d}: loss={}'.format( j+1, EPOCHS, ib+1, dataset.numBatches, i+1, dataset.numSteps, loss ))\n",
+    "            \n",
+    "            dataset.nextStep()\n",
+    "\n",
+    "        dataset.nextBatch()\n",
+    "\n",
+    "    if j%10==9: network.save('./nn_epoch{:04d}.h5'.format(j+1))\n",
+    "\n",
+    "# all done! save final version\n",
+    "network.save('./nn_final.h5'); print(\"Training done, saved NN\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "swG7GeDpWT_Z"
+   },
+   "source": [
+    "The loss should go down from above 1000 initially to below 10. This is a good sign, but of course it's even more important to see how the NN-solver combination fares on new inputs. With this training approach we've realized a hybrid solver, consisting of a regular _source_ simulator, and a network that was trained to specifically interact with this simulator for a chosen domain of simulation cases.\n",
+    "\n",
+    "Let's see how well this works by applying it to a set of test data inputs with new Reynolds numbers that were not part of the training data.\n",
+    "\n",
+    "To keep things somewhat simple, we won't aim for a high-performance version of our hybrid solver. For performance, please check out the external code base: the network trained here should be directly useable in [this apply script](https://github.com/tum-pbs/Solver-in-the-Loop/blob/master/karman-2d/karman_apply.py).\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0c38ne0UdIxV"
+   },
+   "source": [
+    "## Evaluation \n",
+    "\n",
+    "In order to evaluate the performance of our DL-powered solver, we essentially only need to repeat the inner loop of each training iteration for more steps. While we were limited to `msteps` evaluations at training time, we can now run our solver for arbitrary lengths. This is a good test for how well our solver has learned to keep the data within the desired distribution, and represents a generalization test for longer rollouts.\n",
+    "\n",
+    "We reuse the solver code from above, but in the following, we will consider two simulated versions: for comparison, we'll run one reference simulation in the _source_ space (i.e., without any modifications). This version receives the regular outputs of each evaluation of the simulator, and ignores the learned correction (stored in `steps_source` below). The second version, repeatedly computes the source solver plus the learned correction, and advances this state in the solver (`steps_hybrid`).\n",
+    "\n",
+    "We also need a set of new data. Below, we'll download a new set of Reynolds numbers (in between the ones used for training), on which we will later on run the unmodified simulator and the DL-powered one.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "RumKebW_05xp"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading test data (38MB), this can take a moment the first time...\n",
+      "Loaded test data, 4 training sims\n"
+     ]
+    }
+   ],
+   "source": [
+    "fname_test = 'sol-karman-2d-test.pickle'\n",
+    "if not os.path.isfile(fname_test):\n",
+    "  print(\"Downloading test data (38MB), this can take a moment the first time...\")\n",
+    "  urllib.request.urlretrieve(\"https://physicsbaseddeeplearning.org/data/\"+fname_test, fname_test)\n",
+    "\n",
+    "with open(fname_test, 'rb') as f: data_test_preloaded = pickle.load(f)\n",
+    "print(\"Loaded test data, {} training sims\".format(len(data_test_preloaded)) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rZ9h-gRddIxb"
+   },
+   "source": [
+    "Next we create a new dataset object `dataset_test` that organizes the data. We're simply using the first batch of the unshuffled dataset, though.\n",
+    "\n",
+    "A subtle but important point: we still have to use the normalization from the original training data set: `dataset.dataStats['std']` values. The test data set has it's own mean and standard deviation, and so the trained NN never saw this data before. The NN was trained with the data in `dataset` above, and hence we have to use the constants from there for normalization to make sure the network receives values that it can relate to the data it was trained with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "id": "9OPruTGMdIxe"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reynolds numbers in test data set: \u001b[94m(120000.000, 480000.000, 1920000.000, 7680000.000)\u001b[0m along \u001b[92mbatchᵇ\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_test = Dataset( data_preloaded=data_test_preloaded, is_testset=True, num_frames=simsteps, num_sims=4, batch_size=4 )\n",
+    "\n",
+    "# we only need 1 batch with t=0 states to initialize the test simulations with\n",
+    "dataset_test.newEpoch(shuffle_data=False)\n",
+    "batch = getData(dataset_test, consecutive_frames=0) \n",
+    "\n",
+    "re_nr_test = math.tensor(batch[3], math.batch('batch')) # Reynolds numbers\n",
+    "print(\"Reynolds numbers in test data set: \"+format(re_nr_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sMqRPg2pdIxh"
+   },
+   "source": [
+    "Next we construct a `math.tensor` as initial state for the centered marker fields, and a staggered grid from the next two indices of the test set batch. Similar to `to_phiflow` above, we use `phi.math.stack()` to combine two fields of appropriate size as a staggered grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "xK1MEaPqdIxi"
+   },
+   "outputs": [],
+   "source": [
+    "source_dens_initial = math.tensor( batch[0][0], math.batch('batch'), math.spatial('y, x'))\n",
+    "\n",
+    "source_vel_initial = domain.staggered_grid(phi.math.stack([\n",
+    "    math.tensor(batch[2][0], math.batch('batch'),math.spatial('y, x')),\n",
+    "    math.tensor(batch[1][0], math.batch('batch'),math.spatial('y, x'))], channel(vector=\"y,x\")) )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KhGVceo6dIxl"
+   },
+   "source": [
+    "Now we first run the _source_ simulation for 120 steps as baseline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "nbTTl15kdIxl"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Source simulation steps 101\n"
+     ]
+    }
+   ],
+   "source": [
+    "source_dens_test, source_vel_test = source_dens_initial, source_vel_initial\n",
+    "steps_source = [[source_dens_test,source_vel_test]]\n",
+    "\n",
+    "#NT_DEBUG reduce steps\n",
+    "STEPS=120 # 100 enough?\n",
+    "STEPS=100\n",
+    "\n",
+    "# note - math.jit_compile() not useful for numpy solve... hence not necessary here\n",
+    "for i in range(STEPS):\n",
+    "    [source_dens_test,source_vel_test] = simulator.step(\n",
+    "        density_in=source_dens_test,\n",
+    "        velocity_in=source_vel_test,\n",
+    "        re=re_nr_test,\n",
+    "        res=source_res[1],\n",
+    "    )\n",
+    "    steps_source.append( [source_dens_test,source_vel_test] )\n",
+    "\n",
+    "print(\"Source simulation steps \"+format(len(steps_source)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vQV0qV5pdIxm"
+   },
+   "source": [
+    "Next, we compute the corresponding states of our learned hybrid solver. Here, we closely follow the training code, however, now without any gradient tapes or loss computations. We only evaluate the NN in a forward pass for each simulated state to compute a correction field:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "id": "fH5tFfh9dIxn"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Steps with hybrid solver 101\n"
+     ]
+    }
+   ],
+   "source": [
+    "source_dens_test, source_vel_test = source_dens_initial, source_vel_initial\n",
+    "steps_hybrid = [[source_dens_test,source_vel_test]]\n",
+    "        \n",
+    "for i in range(STEPS):\n",
+    "    [source_dens_test,source_vel_test] = simulator.step(\n",
+    "        density_in=source_dens_test,\n",
+    "        velocity_in=source_vel_test,\n",
+    "        re=math.tensor(re_nr_test),\n",
+    "        res=source_res[1],\n",
+    "    )\n",
+    "    model_input = to_keras([source_dens_test,source_vel_test], re_nr_test )\n",
+    "    model_input /= math.tensor([dataset.dataStats['std'][1], dataset.dataStats['std'][2], dataset.dataStats['ext.std'][0]], channel('channels')) # [u, v, Re]\n",
+    "    model_out = network(model_input.native(['batch', 'y', 'x', 'channels']), training=False)\n",
+    "    model_out *= [dataset.dataStats['std'][1], dataset.dataStats['std'][2]] # [u, v]\n",
+    "    correction =  to_phiflow(model_out, domain) \n",
+    "    source_vel_test = source_vel_test + correction\n",
+    "\n",
+    "    steps_hybrid.append( [source_dens_test, source_vel_test] )\n",
+    "    \n",
+    "print(\"Steps with hybrid solver \"+format(len(steps_hybrid)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tnHYeOfldIxp"
+   },
+   "source": [
+    "Given the stored states, we quantify the improvements that the NN yields, and visualize the results. \n",
+    "\n",
+    "In the following cells, the index `b` chooses one of the four test simulations (by default index 0, the lowest Re outside the training data range), and computes the accumulated mean absolute error (MAE) over all time steps.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "bU-PwcCCdIxq"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE for source: 0.12054027616977692 , and hybrid: 0.04435234144330025\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pylab\n",
+    "b = 0 # batch index for the following comparisons\n",
+    "\n",
+    "errors_source, errors_pred = [], []\n",
+    "for index in range(STEPS):\n",
+    "  vx_ref = dataset_test.dataPreloaded[ dataset_test.dataSims[b] ][ index ][1][0,...]\n",
+    "  vy_ref = dataset_test.dataPreloaded[ dataset_test.dataSims[b] ][ index ][2][0,...]\n",
+    "  vxs = vx_ref - steps_source[index][1].values.vector[1].numpy('batch,y,x')[b,...]\n",
+    "  vxh = vx_ref - steps_hybrid[index][1].values.vector[1].numpy('batch,y,x')[b,...]\n",
+    "  vys = vy_ref - steps_source[index][1].values.vector[0].numpy('batch,y,x')[b,...] \n",
+    "  vyh = vy_ref - steps_hybrid[index][1].values.vector[0].numpy('batch,y,x')[b,...] \n",
+    "  errors_source.append(np.mean(np.abs(vxs)) + np.mean(np.abs(vys))) \n",
+    "  errors_pred.append(np.mean(np.abs(vxh)) + np.mean(np.abs(vyh)))\n",
+    "\n",
+    "fig = pylab.figure().gca()\n",
+    "pltx = np.linspace(0,STEPS-1,STEPS)\n",
+    "fig.plot(pltx, errors_source, lw=2, color='mediumblue', label='Source')  \n",
+    "fig.plot(pltx, errors_pred,   lw=2, color='green', label='Hybrid')\n",
+    "pylab.xlabel('Time step'); pylab.ylabel('Error'); fig.legend()\n",
+    "\n",
+    "print(\"MAE for source: \"+format(np.mean(errors_source)) +\" , and hybrid: \"+format(np.mean(errors_pred)) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aOQP6iCBdIxs"
+   },
+   "source": [
+    "Due to the complexity of the training, the performance varies but typically the overall MAE is ca. $2.5\\times$ larger for the regular simulation compared to the hybrid simulator. The graph above also shows this behavior over time.\n",
+    "The gap is usually even larger for other Reynolds numbers within the training data range (try other values for `b` above). \n",
+    "\n",
+    "Let's also visualize the differences of the two outputs by plotting the y component of the velocities over time. The two following code cells show six velocity snapshots for the batch index `b` in intervals of 20 time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "_3f8uhIIdIxs"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = 0          # channel selector, x=1 or y=0 \n",
+    "interval = 20  # time interval\n",
+    "IMGS = STEPS//20+1\n",
+    "\n",
+    "fig, axes = pylab.subplots(1, IMGS, figsize=(16, 5))    \n",
+    "for i in range(0,IMGS):\n",
+    "  v = steps_source[i*interval][1].values.vector[c].numpy('batch,y,x')[b,...]\n",
+    "  axes[i].imshow( v , origin='lower', cmap='magma')\n",
+    "  axes[i].set_title(f\" Source simulation t={i*interval} \")\n",
+    "\n",
+    "pylab.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "v2d2WTGedIxt"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = pylab.subplots(1, IMGS, figsize=(16, 5))\n",
+    "for i in range(0,IMGS):\n",
+    "  v = steps_hybrid[i*interval][1].values.vector[c].numpy('batch,y,x')[b,...]\n",
+    "  axes[i].imshow( v , origin='lower', cmap='magma')\n",
+    "  axes[i].set_title(f\" Hybrid solver t={i*interval} \")\n",
+    "pylab.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ivS0SUiYdIxt"
+   },
+   "source": [
+    "They both start out with the same initial state at $t=0$ (the downsampled solution from the reference solution manifold), and at $t=20$ the solutions still share similarities. Over time, the source version strongly diffuses the structures in the flow and looses momentum. The flow behind the obstacles becomes straight, and lacks clear vortices. \n",
+    "\n",
+    "The version produced by the hybrid solver does much better. It preserves the vortex shedding even after more than one hundred updates. Note that both outputs were produced by the same underlying solver. The second version just profits from the learned corrector which manages to revert the numerical errors of the source solver, including its overly strong dissipation. \n",
+    "\n",
+    "We also visually compare how the NN does w.r.t. reference data. The next cell plots one time step of the three versions: the reference data after 50 steps, and the re-simulated version of the source and our hybrid solver, together with a per-cell error of the two:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "23yyfljqdIxu"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "index = STEPS//2 # time step index\n",
+    "vx_ref = dataset_test.dataPreloaded[ dataset_test.dataSims[b] ][ index ][1][0,...]\n",
+    "vx_src = steps_source[index][1].values.vector[1].numpy('batch,y,x')[b,...]\n",
+    "vx_hyb = steps_hybrid[index][1].values.vector[1].numpy('batch,y,x')[b,...]\n",
+    "\n",
+    "fig, axes = pylab.subplots(1, 4, figsize=(14, 5))\n",
+    "\n",
+    "axes[0].imshow( vx_ref , origin='lower', cmap='magma')\n",
+    "axes[0].set_title(f\" Reference \")\n",
+    "\n",
+    "axes[1].imshow( vx_src , origin='lower', cmap='magma')\n",
+    "axes[1].set_title(f\" Source \")\n",
+    "\n",
+    "axes[2].imshow( vx_hyb , origin='lower', cmap='magma')\n",
+    "axes[2].set_title(f\" Learned \")\n",
+    "\n",
+    "# show error side by side\n",
+    "err_source = vx_ref - vx_src \n",
+    "err_hybrid = vx_ref - vx_hyb \n",
+    "v = np.concatenate([err_source,err_hybrid], axis=1)\n",
+    "axes[3].imshow( v , origin='lower', cmap='cividis')\n",
+    "axes[3].set_title(f\" Errors: Source & Learned\")\n",
+    "\n",
+    "pylab.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BZByQsAydIxv"
+   },
+   "source": [
+    "This shows very clearly how the pure source simulation in the middle deviates from the reference on the left. The learned version stays much closer to the reference solution. \n",
+    "\n",
+    "The two per-cell error images on the right also illustrate this: the source version has much larger errors (i.e. brighter colors) that show how it systematically underestimates the vortices that should form. The error for the learned version is much more evenly distributed and significantly smaller in magnitude.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UQTY8m6LdIxv"
+   },
+   "source": [
+    "This concludes our evaluation. Note that the improved behavior of the hybrid solver can be difficult to reliably measure with simple vector norms such as an MAE or $L^2$ norm. To improve this, we'd need to employ other, domain-specific metrics. In this case, metrics for fluids based on vorticity and turbulence properties of the flow would be applicable. However, in this text, we instead want to focus on DL-related topics and target another inverse problem with differentiable physics solvers in the next chapter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Dl3vzF_XdIxv"
+   },
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "* Modify the training to further reduce the training error. With the _medium_ network you should be able to get the loss down to around 1.\n",
+    "\n",
+    "* Turn off the differentiable physics training (by setting `msteps=1`), and compare it with the DP version.\n",
+    "\n",
+    "* Likewise, train a network with a larger `msteps` setting, e.g., 8 or 16. Note that due to the recurrent nature of the training, you'll probably have to load a pre-trained state to stabilize the first iterations.\n",
+    "\n",
+    "* Use the external github code to generate new test data, and run your trained NN on these cases. You'll see that a reduced training error not always directly correlates with an improved test performance.\n",
+    "\n"
+   ]
+  }
+ ],
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