diff --git a/diffphys-code-sol.ipynb b/diffphys-code-sol.ipynb
index 302c9f6..e6cc3ed 100644
--- a/diffphys-code-sol.ipynb
+++ b/diffphys-code-sol.ipynb
@@ -139,7 +139,7 @@
     "    !pip install --upgrade --quiet phiflow==3.2\n",
     "    #!pip install --upgrade --quiet git+https://github.com/tum-pbs/PhiFlow@develop\n",
     "    # for pbdl-dataset:\n",
-    "    !pip install --upgrade --quiet git+https://github.com/tum-pbs/pbdl-dataset@develop\n",
+    "    !pip install --upgrade --quiet git+https://github.com/tum-pbs/pbdl-dataset\n",
     "\n",
     "except ImportError:\n",
     "    print(\"This notebook is running locally, please make sure the necessary pip packages are installed.\")\n",
diff --git a/overview-ns-forw.ipynb b/overview-ns-forw.ipynb
index 0b018b0..7a41348 100644
--- a/overview-ns-forw.ipynb
+++ b/overview-ns-forw.ipynb
@@ -126,31 +126,31 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Max. velocity and mean marker density: [\u001b[94m0.1558497\u001b[0m, \u001b[94m0.008125\u001b[0m]\n"
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "<matplotlib.image.AxesImage at 0x7ebc74337340>"
             ]
           },
+          "execution_count": 32,
           "metadata": {},
-          "execution_count": 32
+          "output_type": "execute_result"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAAGdCAYAAACmQjDKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAcO0lEQVR4nO3df2xV9f3H8ddF6BVs762ltLd3/bECCiqWZQzrjcrUVkpNCAgm+GOxbAQDK2SATu3iL9yWMkycuiEuMYEtsbBhrEQTYAK2xK2w0dFUdDa069Ya2uJIem+52Atf+vn+gV69UqC3vZ/ee93zkZyk955zz31z/nh6PD331mGMMQIAxNSYeA8AAN9ExBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwYGy8B/i6gYEBHT9+XGlpaXI4HPEeBwDCjDHq6+uT1+vVmDGXPjdNuLgeP35ceXl58R4DAC6qs7NTubm5l9wm4eKalpb2+U+OzxcASBRGkvlKpy4u4eL65aUA4gogEZkhXbLkF1oAYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgAXEFAAuIKwBYQFwBwALiCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgAXEFAAuiiuvmzZtVVFQkl8sll8sln8+nXbt2hdfffvvtcjgcEcuKFStiPjQAJLqx0Wycm5urDRs26JprrpExRr///e+1YMECHTlyRDfccIMkafny5XruuefCr5kwYUJsJwaAJBBVXOfPnx/x+Je//KU2b96sgwcPhuM6YcIEeTye2E0IAElo2Ndcz507p+3btysYDMrn84Wff/3115WZmakZM2aoqqpKp0+fvuR+QqGQAoFAxAIAyS6qM1dJ+uCDD+Tz+dTf36/U1FTV1tbq+uuvlyQ98MADKigokNfrVXNzsx5//HG1tLTozTffvOj+qqurtX79+uH/CwAgATmMMSaaF5w5c0YdHR3y+/1644039Nprr6m+vj4c2K/av3+/SkpK1NraqilTpgy6v1AopFAoFH4cCASUl5en8yfVjqj+MQBgl5E0IL/fL5fLdckto47r15WWlmrKlCn63e9+d8G6YDCo1NRU7d69W2VlZUPaXyAQkNvtFnEFkHiGHtcR3+c6MDAQceb5VU1NTZKknJyckb4NACSVqK65VlVVqby8XPn5+err61NNTY3q6uq0Z88etbW1qaamRnfffbcmTpyo5uZmrV27VnPmzFFRUZGt+QEgIUUV1xMnTuihhx5SV1eX3G63ioqKtGfPHt11113q7OzU3r179eKLLyoYDCovL0+LFy/Wk08+aWt2AEhYI77mGmtccwWQuEbxmisA4ELEFQAsIK4AYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgAXEFAAuIKwBYQFwBwALiCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgQVRx3bx5s4qKiuRyueRyueTz+bRr167w+v7+flVWVmrixIlKTU3V4sWL1dPTE/OhASDRRRXX3NxcbdiwQY2NjTp8+LDuvPNOLViwQB9++KEkae3atXr77be1Y8cO1dfX6/jx41q0aJGVwQEgkTmMMWYkO8jIyNDzzz+ve++9V5MmTVJNTY3uvfdeSdLHH3+s6667Tg0NDbr55puHtL9AICC3263z3XeMZDQAiDEjaUB+v18ul+uSWw77muu5c+e0fft2BYNB+Xw+NTY26uzZsyotLQ1vM336dOXn56uhoeGi+wmFQgoEAhELACS7qOP6wQcfKDU1VU6nUytWrFBtba2uv/56dXd3KyUlRenp6RHbZ2dnq7u7+6L7q66ultvtDi95eXlR/yMAINFEHddp06apqalJhw4d0sqVK1VRUaGPPvpo2ANUVVXJ7/eHl87OzmHvCwASxdhoX5CSkqKpU6dKkmbNmqW///3veumll7RkyRKdOXNGvb29EWevPT098ng8F92f0+mU0+mMfnIASGAjvs91YGBAoVBIs2bN0rhx47Rv377wupaWFnV0dMjn8430bQAgqUR15lpVVaXy8nLl5+err69PNTU1qqur0549e+R2u7Vs2TKtW7dOGRkZcrlcWr16tXw+35DvFACAb4qo4nrixAk99NBD6urqktvtVlFRkfbs2aO77rpLkvTrX/9aY8aM0eLFixUKhVRWVqZXXnnFyuAAkMhGfJ9rrHGfK4DENQr3uQIALo64AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgAXEFAAuIKwBYQFwBwALiCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsiCqu1dXVmj17ttLS0pSVlaWFCxeqpaUlYpvbb79dDocjYlmxYkVMhwaARBdVXOvr61VZWamDBw/q3Xff1dmzZzV37lwFg8GI7ZYvX66urq7wsnHjxpgODQCJbmw0G+/evTvi8datW5WVlaXGxkbNmTMn/PyECRPk8XhiMyEAJKERXXP1+/2SpIyMjIjnX3/9dWVmZmrGjBmqqqrS6dOnR/I2AJB0ojpz/aqBgQGtWbNGt9xyi2bMmBF+/oEHHlBBQYG8Xq+am5v1+OOPq6WlRW+++eag+wmFQgqFQuHHgUBguCMBQMJwGGPMcF64cuVK7dq1S++//75yc3Mvut3+/ftVUlKi1tZWTZky5YL1zz77rNavXz/IK8dIcgxnNACwxEgakN/vl8vluuSWw4rrqlWrtHPnTh04cECFhYWX3DYYDCo1NVW7d+9WWVnZBesHO3PNy8sTcQWQeIYe16guCxhjtHr1atXW1qquru6yYZWkpqYmSVJOTs6g651Op5xOZzRjAEDCiyqulZWVqqmp0c6dO5WWlqbu7m5Jktvt1vjx49XW1qaamhrdfffdmjhxopqbm7V27VrNmTNHRUVFVv4BAJCIoros4HAM/r/pW7Zs0dKlS9XZ2akf/OAHOnr0qILBoPLy8nTPPffoySefvOwp9BcCgYDcbre4LAAg8Vi+5moTcQWQuIYeV75bAAAsIK4AYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgAXEFAAuIKwBYQFwBwALiCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgAXEFAAuIKwBYEFVcq6urNXv2bKWlpSkrK0sLFy5US0tLxDb9/f2qrKzUxIkTlZqaqsWLF6unpyemQwNAoosqrvX19aqsrNTBgwf17rvv6uzZs5o7d66CwWB4m7Vr1+rtt9/Wjh07VF9fr+PHj2vRokUxHxwAEpnDGGOG++JPP/1UWVlZqq+v15w5c+T3+zVp0iTV1NTo3nvvlSR9/PHHuu6669TQ0KCbb775svsMBAJyu906333HcEcDAAuMpAH5/X65XK5Lbjmia65+v1+SlJGRIUlqbGzU2bNnVVpaGt5m+vTpys/PV0NDw6D7CIVCCgQCEQsAJLthx3VgYEBr1qzRLbfcohkzZkiSuru7lZKSovT09Ihts7Oz1d3dPeh+qqur5Xa7w0teXt5wRwKAhDHsuFZWVuro0aPavn37iAaoqqqS3+8PL52dnSPaHwAkgrHDedGqVav0zjvv6MCBA8rNzQ0/7/F4dObMGfX29kacvfb09Mjj8Qy6L6fTKafTOZwxACBhRXXmaozRqlWrVFtbq/3796uwsDBi/axZszRu3Djt27cv/FxLS4s6Ojrk8/liMzEAJIGozlwrKytVU1OjnTt3Ki0tLXwd1e12a/z48XK73Vq2bJnWrVunjIwMuVwurV69Wj6fb0h3CgDAN0VUt2I5HIPfGrVlyxYtXbpU0vkPETzyyCPatm2bQqGQysrK9Morr1z0ssDXcSsWgMQ19FuxRnSfqw3EFUDiGqX7XAEAgyOuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgAXEFAAuIKwBYQFwBwALiCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYAFxBQALoo7rgQMHNH/+fHm9XjkcDr311lsR65cuXSqHwxGxzJs3L1bzAkBSiDquwWBQM2fO1KZNmy66zbx589TV1RVetm3bNqIhASDZjI32BeXl5SovL7/kNk6nUx6PZ9hDAUCys3LNta6uTllZWZo2bZpWrlypkydPXnTbUCikQCAQsQBAsot5XOfNm6c//OEP2rdvn371q1+pvr5e5eXlOnfu3KDbV1dXy+12h5e8vLxYjwQAo85hjDHDfrHDodraWi1cuPCi2/zrX//SlClTtHfvXpWUlFywPhQKKRQKhR8HAoHPAztGkmO4owGABUbSgPx+v1wu1yW3tH4r1uTJk5WZmanW1tZB1zudTrlcrogFAJKd9bh+8sknOnnypHJycmy/FQAkjKjvFjh16lTEWWh7e7uampqUkZGhjIwMrV+/XosXL5bH41FbW5see+wxTZ06VWVlZTEdHAASWdTXXOvq6nTHHXdc8HxFRYU2b96shQsX6siRI+rt7ZXX69XcuXP185//XNnZ2UPafyAQkNvtFtdcASSeoV9zHdEvtGwgrgASVwL9QgsA/hcRVwCwgLgCgAXEFQAsIK4AYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgAXEFAAuIKwBYQFwBwALiCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYAFxBQALiCsAWEBcAcAC4goAFhBXALCAuAKABVHH9cCBA5o/f768Xq8cDofeeuutiPXGGD399NPKycnR+PHjVVpaqmPHjsVqXgBIClHHNRgMaubMmdq0adOg6zdu3KiXX35Zr776qg4dOqSrrrpKZWVl6u/vH/GwAJAsHMYYM+wXOxyqra3VwoULJZ0/a/V6vXrkkUf06KOPSpL8fr+ys7O1detW3XfffZfdZyAQkNvt1vnuO4Y7GgBYYCQNyO/3y+VyXXLLmF5zbW9vV3d3t0pLS8PPud1uFRcXq6GhYdDXhEIhBQKBiAUAkl1M49rd3S1Jys7Ojng+Ozs7vO7rqqur5Xa7w0teXl4sRwKAuIj73QJVVVXy+/3hpbOzM94jAcCIxTSuHo9HktTT0xPxfE9PT3jd1zmdTrlcrogFAJJdTONaWFgoj8ejffv2hZ8LBAI6dOiQfD5fLN8KABLa2GhfcOrUKbW2toYft7e3q6mpSRkZGcrPz9eaNWv0i1/8Qtdcc40KCwv11FNPyev1hu8oAID/BVHH9fDhw7rjjjvCj9etWydJqqio0NatW/XYY48pGAzq4YcfVm9vr2699Vbt3r1bV155ZeymRkyd/b+98R7hAuPGll5+IyCBjeg+Vxu4z3X0EVdgqOJ0nysA4DziCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC6L+EAGSSyLewzoUQ52b+2GRqDhzBQALiCsAWEBcAcAC4goAFhBXALCAuAKABcQVACwgrgBgAR8iSGLJ+gGBWBrKMeCDBogHzlwBwALiCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYAFxBQALiCsAWEBcAcAC4goAFsQ8rs8++6wcDkfEMn369Fi/DQAkNCt/Q+uGG27Q3r1f/m2jsWP5U10A/rdYqd7YsWPl8Xhs7BoAkoKVa67Hjh2T1+vV5MmT9eCDD6qjo+Oi24ZCIQUCgYgFAJJdzONaXFysrVu3avfu3dq8ebPa29t12223qa+vb9Dtq6ur5Xa7w0teXl6sRwKAUecwxhibb9Db26uCggK98MILWrZs2QXrQ6GQQqFQ+HEgEPg8sGMkOWyOlvTO/t/ey28EjRtbGu8R8I1hJA3I7/fL5XJdckvrv2lKT0/Xtddeq9bW1kHXO51OOZ1O22MAwKiyfp/rqVOn1NbWppycHNtvBQAJI+ZxffTRR1VfX69///vf+utf/6p77rlHV1xxhe6///5YvxUAJKyYXxb45JNPdP/99+vkyZOaNGmSbr31Vh08eFCTJk2K9VsBQMKKeVy3b98e610CQNLhuwUAwALiCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC/gW6yQ2lC8k+aZ/uQtfyoJExZkrAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYAFxBQAL+BDBN1yyftCADwcg2XHmCgAWEFcAsIC4AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABbwCS3waSjAAs5cAcAC4goAFhBXALCAuAKABcQVACwgrgBgAXEFAAuIKwBYYC2umzZt0re//W1deeWVKi4u1t/+9jdbbwUACcdKXP/4xz9q3bp1euaZZ/SPf/xDM2fOVFlZmU6cOGHj7QAg4TiMMSbWOy0uLtbs2bP129/+VpI0MDCgvLw8rV69Wk888cQlXxsIBOR2u3W++45YjwYAI2AkDcjv98vlcl1yy5ifuZ45c0aNjY0qLf3y8+pjxoxRaWmpGhoaYv12AJCQYv7FLf/973917tw5ZWdnRzyfnZ2tjz/++ILtQ6GQQqFQ+HEgEIj1SAAw6uJ+t0B1dbXcbnd4ycvLi/dIADBiMY9rZmamrrjiCvX09EQ839PTI4/Hc8H2VVVV8vv94aWzszPWIwHAqIt5XFNSUjRr1izt27cv/NzAwID27dsnn893wfZOp1MulytiAYBkZ+XLstetW6eKigp973vf00033aQXX3xRwWBQP/zhDy/72i9vXoj5TQwAMELnuzSUm6ysxHXJkiX69NNP9fTTT6u7u1vf+c53tHv37gt+yTWYvr6+z38yIrAAElFfX9/nt4xenJX7XEdiYGBAx48fV1pamhyO8/e5BgIB5eXlqbOzM6kuGzD36EvW2Zl7dA13bmOM+vr65PV6NWbMpa+qJtzf0BozZoxyc3MHXZes12SZe/Ql6+zMPbqGM/flzli/EPdbsQDgm4i4AoAFSRFXp9OpZ555Rk6nM96jRIW5R1+yzs7co2s05k64X2gBwDdBUpy5AkCyIa4AYAFxBQALiCsAWJDwcU3Gv8X17LPPyuFwRCzTp0+P91gXOHDggObPny+v1yuHw6G33norYr0xRk8//bRycnI0fvx4lZaW6tixY/EZ9isuN/fSpUsvOP7z5s2Lz7BfUV1drdmzZystLU1ZWVlauHChWlpaIrbp7+9XZWWlJk6cqNTUVC1evPiCb5gbbUOZ+/bbb7/gmK9YsSJOE5+3efNmFRUVhT8o4PP5tGvXrvB628c6oeOazH+L64YbblBXV1d4ef/99+M90gWCwaBmzpypTZs2Dbp+48aNevnll/Xqq6/q0KFDuuqqq1RWVqb+/v5RnjTS5eaWpHnz5kUc/23bto3ihIOrr69XZWWlDh48qHfffVdnz57V3LlzFQwGw9usXbtWb7/9tnbs2KH6+nodP35cixYtiuPUQ5tbkpYvXx5xzDdu3Binic/Lzc3Vhg0b1NjYqMOHD+vOO+/UggUL9OGHH0oahWNtEthNN91kKisrw4/PnTtnvF6vqa6ujuNUl/fMM8+YmTNnxnuMqEgytbW14ccDAwPG4/GY559/Pvxcb2+vcTqdZtu2bXGYcHBfn9sYYyoqKsyCBQviMk80Tpw4YSSZ+vp6Y8z54ztu3DizY8eO8Db//Oc/jSTT0NAQrzEv8PW5jTHm+9//vvnJT34Sv6GG6OqrrzavvfbaqBzrhD1zTfa/xXXs2DF5vV5NnjxZDz74oDo6OuI9UlTa29vV3d0dcfzdbreKi4uT4vjX1dUpKytL06ZN08qVK3Xy5Ml4j3QBv98vScrIyJAkNTY26uzZsxHHfPr06crPz0+oY/71ub/w+uuvKzMzUzNmzFBVVZVOnz4dj/EGde7cOW3fvl3BYFA+n29UjnXCfXHLF6L9W1yJpLi4WFu3btW0adPU1dWl9evX67bbbtPRo0eVlpYW7/GGpLu7W5IGPf5frEtU8+bN06JFi1RYWKi2tjb97Gc/U3l5uRoaGnTFFVfEezxJ57/9bc2aNbrllls0Y8YMSeePeUpKitLT0yO2TaRjPtjckvTAAw+ooKBAXq9Xzc3Nevzxx9XS0qI333wzjtNKH3zwgXw+n/r7+5Wamqra2lpdf/31ampqsn6sEzauyay8vDz8c1FRkYqLi1VQUKA//elPWrZsWRwn+99w3333hX++8cYbVVRUpClTpqiurk4lJSVxnOxLlZWVOnr0aEJei7+Ui8398MMPh3++8cYblZOTo5KSErW1tWnKlCmjPWbYtGnT1NTUJL/frzfeeEMVFRWqr68flfdO2MsC0f4trkSWnp6ua6+9Vq2trfEeZci+OMbfhOM/efJkZWZmJszxX7Vqld555x299957EV+v6fF4dObMGfX29kZsnyjH/GJzD6a4uFiS4n7MU1JSNHXqVM2aNUvV1dWaOXOmXnrppVE51gkb12j/FlciO3XqlNra2pSTkxPvUYassLBQHo8n4vgHAgEdOnQo6Y7/J598opMnT8b9+BtjtGrVKtXW1mr//v0qLCyMWD9r1iyNGzcu4pi3tLSoo6Mjrsf8cnMPpqmpSZLifsy/bmBgQKFQaHSOdUx+LWbJ9u3bjdPpNFu3bjUfffSRefjhh016errp7u6O92iX9Mgjj5i6ujrT3t5u/vKXv5jS0lKTmZlpTpw4Ee/RIvT19ZkjR46YI0eOGEnmhRdeMEeOHDH/+c9/jDHGbNiwwaSnp5udO3ea5uZms2DBAlNYWGg+++yzhJ27r6/PPProo6ahocG0t7ebvXv3mu9+97vmmmuuMf39/XGde+XKlcbtdpu6ujrT1dUVXk6fPh3eZsWKFSY/P9/s37/fHD582Ph8PuPz+eI49eXnbm1tNc8995w5fPiwaW9vNzt37jSTJ082c+bMievcTzzxhKmvrzft7e2mubnZPPHEE8bhcJg///nPxhj7xzqh42qMMb/5zW9Mfn6+SUlJMTfddJM5ePBgvEe6rCVLlpicnByTkpJivvWtb5klS5aY1tbWeI91gffee++LP1QWsVRUVBhjzt+O9dRTT5ns7GzjdDpNSUmJaWlpie/Q5tJznz592sydO9dMmjTJjBs3zhQUFJjly5cnxH+QB5tZktmyZUt4m88++8z8+Mc/NldffbWZMGGCueeee0xXV1f8hjaXn7ujo8PMmTPHZGRkGKfTaaZOnWp++tOfGr/fH9e5f/SjH5mCggKTkpJiJk2aZEpKSsJhNcb+seYrBwHAgoS95goAyYy4AoAFxBUALCCuAGABcQUAC4grAFhAXAHAAuIKABYQVwCwgLgCgAXEFQAsIK4AYMH/A8Qnpm1BG/wWAAAAAElFTkSuQmCC",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": "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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -194,16 +194,16 @@
       "cell_type": "code",
       "execution_count": 33,
       "metadata": {
-        "id": "XaVmTuhFJyDr",
-        "outputId": "030c48c6-e853-4892-9c1b-116f615b9c94",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "id": "XaVmTuhFJyDr",
+        "outputId": "030c48c6-e853-4892-9c1b-116f615b9c94"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Smoke: (xˢ=32, yˢ=40)\n",
             "Velocity: (xˢ=32, yˢ=40, vectorᶜ=x,y)\n",
@@ -232,16 +232,16 @@
       "cell_type": "code",
       "execution_count": 34,
       "metadata": {
-        "id": "SSVsk77PJyDr",
-        "outputId": "a740eeac-530e-41bb-9b15-a56bdca3998c",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "id": "SSVsk77PJyDr",
+        "outputId": "a740eeac-530e-41bb-9b15-a56bdca3998c"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Shape content: (32, 40, 2)\n",
             "Vector dimension: 2\n"
@@ -266,16 +266,16 @@
       "cell_type": "code",
       "execution_count": 35,
       "metadata": {
-        "id": "9WQAcm3VJyDr",
-        "outputId": "c396e482-da91-4d1e-fece-9f7bf7dd924e",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "id": "9WQAcm3VJyDr",
+        "outputId": "c396e482-da91-4d1e-fece-9f7bf7dd924e"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Statistics of the different simulation grids:\n",
             "\u001b[92m(xˢ=32, yˢ=40)\u001b[0m \u001b[94m0.008 ± 0.039\u001b[0m \u001b[37m(0e+00...2e-01)\u001b[0m\n",
@@ -328,8 +328,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Computed frame 0, max velocity 0.4630011022090912\n",
             "Computed frame 1, max velocity 0.8966455459594727\n",
@@ -372,24 +372,24 @@
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "<matplotlib.image.AxesImage at 0x7ebc7431dd50>"
             ]
           },
+          "execution_count": 37,
           "metadata": {},
-          "execution_count": 37
+          "output_type": "execute_result"
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -418,8 +418,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Computing time step 0\n",
             "Computing time step 1\n",
@@ -428,14 +428,14 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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             "text/plain": [
               "<Figure size 1600x500 with 5 Axes>"
-            ],
-            "image/png": 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+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -462,70 +462,37 @@
         "We can also take a look at the velocities. The `steps` list above already stores `vector[0]` and `vector[1]` components of the velocities as numpy arrays, which we can show next."
       ]
     },
-    {
-      "cell_type": "code",
-      "source": [
-        "#steps[i][1].numpy('y,x')\n",
-        "#type(steps[0][1].shape)\n",
-        "#steps[0][1].shape\n",
-        "#.staggered_tensor().numpy('batch,y,x,vector')[BATCH,:,:,0]\n",
-        "\n",
-        "velocity.vector['x'].numpy('y,x').shape\n",
-        "#vel2 = StaggeredGrid(0, extrapolation.ZERO, x=32, y=40, bounds=Box(x=(0,80),y=(0,100)))\n",
-        "#vel2"
-      ],
-      "metadata": {
-        "id": "0UfjXWXbLDyJ",
-        "outputId": "42f47ce6-8e6f-4069-e9b8-e73526ea131e",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "execution_count": 39,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(40, 31)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 39
-        }
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": 40,
       "metadata": {
-        "id": "tl2YIDYIJyDt",
-        "outputId": "2ac46ab3-b2b6-4b81-8e79-a801f16635a4",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 684
-        }
+        },
+        "id": "tl2YIDYIJyDt",
+        "outputId": "2ac46ab3-b2b6-4b81-8e79-a801f16635a4"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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             "text/plain": [
               "<Figure size 1600x500 with 5 Axes>"
-            ],
-            "image/png": 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+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
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             "text/plain": [
               "<Figure size 1600x500 with 5 Axes>"
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+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -537,8 +504,7 @@
         "fig, axes = pylab.subplots(1, len(steps), figsize=(16, 5))\n",
         "for i in range(len(steps)):\n",
         "    axes[i].imshow(steps[i][2].numpy('y,x'), origin='lower', cmap='magma')\n",
-        "    axes[i].set_title(f\"u_y at t={i*5}\")\n",
-        ""
+        "    axes[i].set_title(f\"u_y at t={i*5}\")\n"
       ]
     },
     {
diff --git a/supervised-airfoils.ipynb b/supervised-airfoils.ipynb
index 2062af9..3b263d5 100644
--- a/supervised-airfoils.ipynb
+++ b/supervised-airfoils.ipynb
@@ -86,7 +86,7 @@
     "import torch.nn as nn\n",
     "import torch.optim as optim\n",
     "\n",
-    "!pip install --upgrade --quiet git+https://github.com/tum-pbs/pbdl-dataset@develop\n",
+    "!pip install --upgrade --quiet git+https://github.com/tum-pbs/pbdl-dataset\n",
     "from pbdl.torch.loader import Dataloader"
    ]
   },