diff --git a/diffphys-code-sol.ipynb b/diffphys-code-sol.ipynb
index 0738ab3..3adba30 100644
--- a/diffphys-code-sol.ipynb
+++ b/diffphys-code-sol.ipynb
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -140,13 +140,13 @@
    "source": [
     "import os, sys, logging, argparse, pickle, glob, random, distutils.dir_util, urllib.request\n",
     "\n",
-    "fname_train = 'sol-data-karman-2d-train.pickle'\n",
+    "fname_train = 'pbdl-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://ge.in.tum.de/download/2020-solver-in-the-loop/\"+fname_train, fname_train)\n",
     "\n",
-    "with open(fname_train, 'rb') as f: dataPreloaded = pickle.load(f)\n",
-    "print(\"Loaded data, {} training sims\".format(len(dataPreloaded)) )\n"
+    "with open(fname_train, 'rb') as f: data_preloaded = pickle.load(f)\n",
+    "print(\"Loaded data, {} training sims\".format(len(data_preloaded)) )\n"
    ]
   },
   {
@@ -155,12 +155,12 @@
     "id": "RY1F4kdWPLNG"
    },
    "source": [
-    "Also let's get installing / importing all the necessary libraries out of the way. And while we're at it, we can set the random seed - obviously, 42 is the ultimate choice here \ud83d\ude42"
+    "Also let's get installing / importing all the necessary libraries out of the way. And while we're at it, we can set the random seed - obviously, 42 is the ultimate choice here 🙂"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -168,32 +168,17 @@
     "id": "BGN4GqxkIueM",
     "outputId": "095adbf8-1ef6-41fd-938e-6cafcf0fdfdc"
    },
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'phi.tf.util'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-7310d213a1a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mphi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflow\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mphi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'phi.tf.util'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "#!pip install --upgrade --quiet phiflow\n",
-    "#%tensorflow_version 1.x\n",
+    "# ??? !pip install --upgrade --quiet phiflow\n",
     "\n",
     "from phi.tf.flow import *\n",
-    "import phi.tf.util\n",
-    "\n",
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
     "\n",
     "random.seed(42)\n",
     "np.random.seed(42)\n",
-    "tf.compat.v1.set_random_seed(42)\n"
+    "tf.random.set_seed(42)"
    ]
   },
   {
@@ -218,10 +203,10 @@
     "height: 200px\n",
     "name: diffphys-sol-domain\n",
     "---\n",
-    "Domain setup for the wake flow case.\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 (`velBCy,velBCyMask`), to set the y components at the bottom of the domain. It then calls `super().step()` to run the _regular_ phiflow fluid solving step.\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:"
    ]
   },
   {
@@ -232,26 +217,37 @@
    },
    "outputs": [],
    "source": [
-    "class KarmanFlow(IncompressibleFlow):\n",
-    "    def __init__(self, pressure_solver=None, make_input_divfree=False, make_output_divfree=True):\n",
-    "        IncompressibleFlow.__init__(self, pressure_solver, make_input_divfree, make_output_divfree)\n",
+    "class KarmanFlow():\n",
+    "    def __init__(self, domain):\n",
+    "        self.domain = domain\n",
     "\n",
-    "        self.infl = Inflow(box[5:10, 25:75])\n",
-    "        self.obst = Obstacle(Sphere([50, 50], 10))\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, fluid, re, res, velBCy, velBCyMask, dt=1.0, gravity=Gravity()):\n",
-    "        # apply viscosity\n",
-    "        alpha = 1.0/math.reshape(re, [fluid._batch_size, 1, 1, 1]) * dt * res * res\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",
-    "        cy = diffuse(CenteredGrid(fluid.velocity.data[0].data), alpha)\n",
-    "        cx = diffuse(CenteredGrid(fluid.velocity.data[1].data), alpha)\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",
-    "        # apply velocity BCs, only for y velocity for now. note: content of velBCy should be pre-multiplied\n",
-    "        cy = cy*(1.0 - velBCyMask) + velBCy\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",
-    "        fluid = fluid.copied_with(velocity=StaggeredGrid([cy.data, cx.data], fluid.velocity.box))\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",
-    "        return super().step(fluid=fluid, dt=dt, obstacles=[self.obst], gravity=gravity, density_effects=[self.infl], velocity_effects=())\n"
+    "    "
    ]
   },
   {
@@ -265,7 +261,7 @@
     "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",
+    "- 2 fields with x,y velocity\n",
     "- the Reynolds number as constant channel.\n",
     "\n",
     "The output is: \n",
@@ -282,13 +278,13 @@
    },
    "outputs": [],
    "source": [
-    "def network_small(tensor_in):\n",
+    "def network_small(inputs_dict):\n",
     "    return keras.Sequential([\n",
-    "        keras.layers.Input(tensor=tensor_in),\n",
+    "        keras.layers.Input(**inputs_dict),\n",
     "        keras.layers.Conv2D(filters=32, kernel_size=5, padding='same', activation=tf.nn.relu),\n",
     "        keras.layers.Conv2D(filters=64, kernel_size=5, padding='same', activation=tf.nn.relu),\n",
-    "        keras.layers.Conv2D(filters=2,  kernel_size=5, padding='same', activation=None),  # u, v\n",
-    "    ])"
+    "        keras.layers.Conv2D(filters=2,  kernel_size=5, padding='same', activation=None), # u, v\n",
+    "    ], name='net_small')"
    ]
   },
   {
@@ -308,8 +304,8 @@
    },
    "outputs": [],
    "source": [
-    "def network_medium(tensor_in):\n",
-    "    l_input = keras.layers.Input(tensor=tensor_in)\n",
+    "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",
@@ -355,11 +351,12 @@
    "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 `staggered_tensor` from phiflow (a 2 component tensor instead of 2 separate grids), from which we discard the outermost layer. We then add a constant channel via `tf.constant` that is multiplied by the desired Reynolds number. The resulting stack of grids is provided as a tensor as input to the neural network. \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 can be combined. \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_staggered` function. \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 `unstack_staggered_tensor` function in `StaggeredGrid()` constructor)."
+    "(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)."
    ]
   },
   {
@@ -370,18 +367,33 @@
    },
    "outputs": [],
    "source": [
-    "def to_keras(fluidstate, ext_const_channel):\n",
-    "  # drop the unused edges of the staggered velocity grid making its size the same as the centered grid\n",
-    "  return math.concat(\n",
-    "      [\n",
-    "          fluidstate.velocity.staggered_tensor()[:, :-1:, :-1:, 0:2],\n",
-    "          tf.constant(shape=fluidstate.density.data.shape, value=1.0)*math.reshape(value=ext_const_channel, shape=[fluidstate._batch_size, 1, 1, 1]),\n",
-    "      ],\n",
-    "      axis=-1\n",
-    "  )\n",
     "\n",
-    "def to_staggered(tensor_cen, box):\n",
-    "  return StaggeredGrid(math.pad(tensor_cen, ((0,0), (0,1), (0,1), (0,0))), box=box)\n"
+    "def to_keras(dens_vel_grid_array, ext_const_channel):\n",
+    "    # drop the unused edges of the staggered velocity grid making its dim same to the centered grid's\n",
+    "    return math.stack(\n",
+    "        [\n",
+    "            #dens_vel_grid_array[1].vector['x'].x[:-1].values,         # u\n",
+    "            # ? tf.pad( dens_vel_grid_array[1].vector['x'].values, [(0,0), (0,0), (0,1)] ),  \n",
+    "            # ? tf.pad( dens_vel_grid_array[1].native(['batch', 'y', 'x']), [(0,0), (0,0), (0,1)] ),  \n",
+    "            # works! tf.pad( dens_vel_grid_array[1].vector['x'].values.native(['batch', 'y', 'x']), [(0,0), (0,0), (0,1)] ),  \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.pad(tf_tensor[..., 0], [(0,0), (0,0), (0,1)]), math.batch('batch'), math.spatial('y, x')), # u\n",
+    "                # NT_DEBUG check\n",
+    "                math.tensor( tf_tensor[...,:-1, 0], math.batch('batch'), math.spatial('y, x')), # u \n",
+    "            ], math.channel('vector')\n",
+    "        )\n",
+    "    )\n"
    ]
   },
   {
@@ -434,36 +446,41 @@
     "            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-reduced/sim_%06d'%i for i in range(num_sims) ]\n",
-    "            ReNrs = [240000.0, 960000.0, 3840000.0] \n",
+    "            self.dataSims = ['karman-fdt-hires-testset/sim_%06d'%i for i in range(num_sims) ]\n",
+    "            #ReNrs = [240000.0, 480000.0, 960000.0, 1920000.0, 3840000.0] \n",
+    "            #ReNrs = [120000.0, 240000.0, 480000.0, 960000.0, 1920000.0, 3840000.0, 7680000.0] # new extende\n",
+    "            ReNrs = [120000.0, 480000.0, 1920000.0, 7680000.0] # new reduced to 4\n",
     "            self.extConstChannelPerSim = { self.dataSims[i]:[ReNrs[i]] for i in range(num_sims) }\n",
     "\n",
-    "        self.dataFrms = [ np.arange(num_frames) for _ in self.dataSims ]  \n",
-    "\n",
-    "        #print(format(self.dataPreloaded[self.dataSims[0]][0][0].shape )) # debugging example: check shape of a single marker density field\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)),  # marker density\n",
-    "                (\n",
-    "                    np.std(np.concatenate([np.absolute(self.dataPreloaded[asim][i][1][...,0].reshape(-1)) for asim in self.dataSims for i in range(num_frames)])),  # vel[0]\n",
-    "                    np.std(np.concatenate([np.absolute(self.dataPreloaded[asim][i][1][...,1].reshape(-1)) for asim in self.dataSims for i in range(num_frames)])),  # vel[1]\n",
-    "                )\n",
-    "            ),\n",
-    "            'ext.std': [ np.std([np.absolute(self.extConstChannelPerSim[asim][0]) for asim in self.dataSims]) ] # Re\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.dataFrms[i][0:(len(self.dataFrms[i])-exclude_tail)]) for i,asim in enumerate(self.dataSims) ]\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",
@@ -488,7 +505,7 @@
     "id": "twIMJ3V0N1FX"
    },
    "source": [
-    "The `nextEpoch`, `nextBatch`, and `nextStep` functions will be called at training time to randomize the training data.\n",
+    "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"
    ]
@@ -501,36 +518,44 @@
    },
    "outputs": [],
    "source": [
-    "    # for class Dataset():\n",
-    "\n",
-    "    # get one mini batch of data: [marker density, velocity, Reynolds number] all from ground truth\n",
-    "    def getData(self, consecutive_frames, with_skip=1):\n",
-    "        marker_dens = [\n",
-    "            math.concat([\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*with_skip  # steps\n",
-    "                ][0]\n",
-    "                for i in range(self.batchSize)\n",
-    "            ], axis=0) for j in range(consecutive_frames+1)\n",
-    "        ]\n",
-    "        velocity = [\n",
-    "            math.concat([\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*with_skip  # steps\n",
-    "                ][1]\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 [marker_dens, velocity, ext]\n"
+    "# 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"
    ]
   },
   {
@@ -546,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -554,13 +579,21 @@
     "id": "59EBdEdj0QR2",
     "outputId": "8043f090-4e7b-4178-d2d2-513981e3905b"
    },
-   "outputs": [],
+   "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=dataPreloaded, num_frames=simsteps, num_sims=nsims, batch_size=batch_size )\n"
+    "dataset = Dataset( data_preloaded=data_preloaded, num_frames=simsteps, num_sims=nsims, batch_size=batch_size )\n"
    ]
   },
   {
@@ -573,7 +606,9 @@
     "\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 `source` and `reference` simulations below. The reference is just a placeholder for data, only the `source` simulation is actually executed. We also define the actual NN `network`. All three are initialized with the size given in the data set `dataset.resolution`."
+    "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. "
    ]
   },
   {
@@ -591,63 +626,44 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Instructions for updating:\n",
-      "If using Keras pass *_constraint arguments to layers.\n",
-      "Model: \"sequential\"\n",
+      "Model: \"net_small\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "conv2d (Conv2D)              (3, 64, 32, 32)           2432      \n",
+      "conv2d (Conv2D)              (None, 64, 32, 32)        2432      \n",
       "_________________________________________________________________\n",
-      "conv2d_1 (Conv2D)            (3, 64, 32, 64)           51264     \n",
+      "conv2d_1 (Conv2D)            (None, 64, 32, 64)        51264     \n",
       "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (3, 64, 32, 2)            3202      \n",
+      "conv2d_2 (Conv2D)            (None, 64, 32, 2)         3202      \n",
       "=================================================================\n",
       "Total params: 56,898\n",
       "Trainable params: 56,898\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/thuerey/Library/Python/3.7/lib/python/site-packages/ipykernel_launcher.py:23: DeprecationWarning: placeholder_like may not respect the batch dimension. For State objects, use placeholder(state.shape) instead.\n",
-      "/Users/thuerey/Library/Python/3.7/lib/python/site-packages/ipykernel_launcher.py:24: DeprecationWarning: placeholder_like may not respect the batch dimension. For State objects, use placeholder(state.shape) instead.\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",
+    "# # this is the actual resolution in terms of cells\n",
     "source_res = list(dataset.resolution)\n",
-    "# this is only a virtual size, in terms of abstract units for the bounding box of the domain (in practice it's important for conversions or when rescaling to physical units)\n",
-    "sim_len = 100.\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",
-    "source     =   Fluid(Domain(resolution=source_res, box=box[0:sim_len*2, 0:sim_len], boundaries=OPEN), buoyancy_factor=0, batch_size=batch_size)\n",
-    "reference = [ Fluid(Domain(resolution=source_res, box=box[0:sim_len*2, 0:sim_len], boundaries=OPEN), buoyancy_factor=0, batch_size=batch_size) for _ in range(msteps) ]\n",
+    "# for readability\n",
+    "from phi.physics._boundaries import Domain, OPEN, STICKY as CLOSED\n",
     "\n",
-    "# velocity BC\n",
-    "vn = np.zeros(source.velocity.data[0].data.shape)  # st.velocity.data[0] is considered as the velocity field in y axis!\n",
-    "vn[..., 0:2, 0:vn.shape[2]-1, 0] = 1.0\n",
-    "vn[..., 0:vn.shape[1], 0:1,   0] = 1.0\n",
-    "vn[..., 0:vn.shape[1], -1:,   0] = 1.0\n",
-    "velBCy = vn\n",
-    "velBCyMask = np.copy(vn)  # warning, mask needs to be binary, 0..1, this only works if vel is also 1\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",
-    "lr_in =   tf.placeholder(tf.float32, shape=[])  # learning rate\n",
-    "Re_in =   tf.placeholder(tf.float32, shape=[batch_size])  # Reynolds numbers\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",
-    "source_in =   phi.tf.util.placeholder_like(source)\n",
-    "reference_in = [ phi.tf.util.placeholder_like(source) for _ in range(msteps) ]\n",
-    "\n",
-    "network = network_small(to_keras(source_in, Re_in)) # use small network for testing\n",
-    "#network = network_medium(to_keras(source_in, Re_in)) # optionally switch to larger network\n",
-    "network.summary() \n",
-    "\n"
+    "network = network_small(dict(shape=(source_res[0],source_res[1], 3)))\n",
+    "network.summary()\n"
    ]
   },
   {
@@ -658,45 +674,75 @@
    "source": [
     "## Interleaving simulation and NN\n",
     "\n",
-    "Now comes the **most crucial** step in the whole setup: we define the chain of simulation steps and network evaluations to be used at training time. After all the work defining helper functions, it's actually pretty simple: we loop over `msteps`, call the simulator via `KarmanFlow.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 step `prediction[-1]` with `velocity + correction[-1]`.\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`. This is slightly complicated as we have to append the scaling for the Reynolds numbers to the normalization for the velocity. After evaluating the `network`, we only have a velocity left, so we can simply multiply by the standard deviation of the velocity again (via `* dataset.dataStats['std'][1]`)."
+    "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 can simply multiply 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 can 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": null,
+   "execution_count": 11,
    "metadata": {
     "id": "D5NeMcLGQaxh",
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "prediction, correction = [], []\n",
-    "for i in range(msteps):\n",
-    "    prediction += [\n",
-    "        KarmanFlow().step(\n",
-    "            source_in if i==0 else prediction[-1],\n",
-    "            re=Re_in,\n",
-    "            res=source_res[-1],  # reference resolution is size in x direction\n",
-    "            velBCy=velBCy,\n",
-    "            velBCyMask=velBCyMask\n",
-    "        )\n",
-    "    ]\n",
+    "def training_step(dens_gt, vel_gt, Re, i_step):\n",
+    "    with tf.GradientTape() as tape:\n",
+    "        #prediction, correction = [], [] # predicted states with correction, inferred velocity corrections\n",
+    "        prediction, correction = [ [dens_gt[0],vel_gt[0]] ], [0] # predicted states with correction, inferred velocity corrections\n",
     "\n",
-    "    correction += [\n",
-    "        to_staggered(\n",
-    "            network(\n",
-    "                to_keras(prediction[-1], Re_in)/[\n",
-    "                    *(dataset.dataStats['std'][1]),  # velocity\n",
-    "                    dataset.dataStats['ext.std'][0]  # Re\n",
-    "                ]\n",
-    "            ) * dataset.dataStats['std'][1],\n",
-    "            box=source.velocity.box\n",
-    "        )\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",
-    "    prediction[-1] = prediction[-1].copied_with(velocity=prediction[-1].velocity + correction[-1])\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"
    ]
   },
   {
@@ -705,7 +751,7 @@
     "id": "c4yLlDM3QfUR"
    },
    "source": [
-    "We also need to define a loss function for training. Here, we can simply use an $L^2$ loss over the whole sequence, i.e. the iteration over `msteps`:"
+    "Once defined, we can 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). Using the jit compilation can make a huge difference in terms of runtime."
    ]
   },
   {
@@ -716,14 +762,8 @@
    },
    "outputs": [],
    "source": [
-    "loss_steps = [\n",
-    "    tf.nn.l2_loss(\n",
-    "        (reference_in[i].velocity.staggered_tensor() - prediction[i].velocity.staggered_tensor())\n",
-    "        /dataset.dataStats['std'][1]\n",
-    "    )\n",
-    "    for i in range(msteps)\n",
-    "]\n",
-    "loss = tf.reduce_sum(loss_steps)/msteps\n"
+    "\n",
+    "training_step_jit = math.jit_compile(training_step)\n"
    ]
   },
   {
@@ -734,62 +774,31 @@
    "source": [
     "## Training\n",
     "\n",
-    "For the training, we use a standard Adam optimizer, and only run 4 epochs by default. This should be increased for the larger network or to obtain more accurate results."
+    "For the training, we use a standard Adam optimizer, and only run 5 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": null,
+   "execution_count": 13,
    "metadata": {
     "id": "PuljFamYQksW"
    },
    "outputs": [],
    "source": [
-    "lr = 1e-4\n",
-    "adapt_lr = True\n",
-    "resume = 0 # set to 1 to load existing network\n",
-    "epochs = 4\n",
+    "LR = 1e-4\n",
+    "EPOCHS = 5\n",
     "\n",
-    "opt = tf.compat.v1.train.AdamOptimizer(learning_rate=lr_in)\n",
-    "train_step = opt.minimize(loss)\n",
-    "\n",
-    "tf_session = tf.Session() \n",
-    "scene = Scene.create(\".\", count=batch_size, mkdir=False, copy_calling_script=False) # not really used here\n",
-    "sess = Session(scene, session=tf_session)\n",
-    "tf.compat.v1.keras.backend.set_session(tf_session)\n",
-    "\n",
-    "sess.initialize_variables()\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",
-    "    network.set_weights(ld_network.get_weights())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "p8hUXJDkRQST"
-   },
-   "source": [
-    "As this setups supports an (optional) fairly accurate training with the medium sized network from above, we'll define a helper function for scheduling learning rate decay. This helps to make the network converge later on in the training. The steps below (after 10,15 etc. epochs) were determined heuristically from previous runs, and are not active by the default run with `epochs=4` (feel free to change and experiment to train more accurate networks)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "id": "Am3hSdNgRPEh"
-   },
-   "outputs": [],
-   "source": [
-    "def lr_schedule(epoch, current_lr):\n",
-    "    lr = current_lr\n",
-    "    if   epoch == 25: lr *= 0.5\n",
-    "    elif epoch == 20: lr *= 1e-1\n",
-    "    elif epoch == 15: lr *= 1e-1\n",
-    "    elif epoch == 10: lr *= 1e-1\n",
-    "    return lr\n"
+    "    ld_network = keras.models.load_model('./nn_epoch{:04d}.h5'.format(resume)) \n",
+    "    network.set_weights(ld_network.get_weights())\n",
+    "    "
    ]
   },
   {
@@ -800,45 +809,123 @@
    "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 `final.h5` network from a previous run, you can potentially skip this block and load the previously trained network instead.)"
+    "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": null,
+   "execution_count": 14,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "m3Nd8YyHRVFQ",
-    "outputId": "148d951b-7070-4a95-c6d7-0fd91d29606e"
+    "outputId": "148d951b-7070-4a95-c6d7-0fd91d29606e",
+    "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/thuerey/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/ops/math_grad.py:297: setdiff1d (from tensorflow.python.ops.array_ops) is deprecated and will be removed after 2018-11-30.\n",
+      "Instructions for updating:\n",
+      "This op will be removed after the deprecation date. Please switch to tf.sets.difference().\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/thuerey/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/ops/math_grad.py:297: setdiff1d (from tensorflow.python.ops.array_ops) is deprecated and will be removed after 2018-11-30.\n",
+      "Instructions for updating:\n",
+      "This op will be removed after the deprecation date. Please switch to tf.sets.difference().\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 001/005, batch 001/002, step 0001/0496: loss=2012.807373046875\n",
+      "epoch 001/005, batch 001/002, step 0002/0496: loss=992.9037475585938\n",
+      "epoch 001/005, batch 001/002, step 0003/0496: loss=738.096435546875\n",
+      "epoch 001/005, batch 001/002, step 0033/0496: loss=213.21969604492188\n",
+      "epoch 001/005, batch 001/002, step 0065/0496: loss=143.3897705078125\n",
+      "epoch 001/005, batch 001/002, step 0097/0496: loss=112.1771240234375\n",
+      "epoch 001/005, batch 001/002, step 0129/0496: loss=91.61509704589844\n",
+      "epoch 001/005, batch 001/002, step 0161/0496: loss=100.89246368408203\n",
+      "epoch 001/005, batch 001/002, step 0193/0496: loss=87.17778015136719\n",
+      "epoch 001/005, batch 001/002, step 0225/0496: loss=67.21128845214844\n",
+      "epoch 001/005, batch 001/002, step 0257/0496: loss=64.24838256835938\n",
+      "epoch 001/005, batch 001/002, step 0289/0496: loss=53.7073860168457\n",
+      "epoch 001/005, batch 001/002, step 0321/0496: loss=55.73740768432617\n",
+      "epoch 001/005, batch 001/002, step 0353/0496: loss=49.17698287963867\n",
+      "epoch 001/005, batch 001/002, step 0385/0496: loss=56.05778121948242\n",
+      "epoch 001/005, batch 001/002, step 0417/0496: loss=41.67274856567383\n",
+      "epoch 001/005, batch 001/002, step 0449/0496: loss=46.14646530151367\n",
+      "epoch 001/005, batch 001/002, step 0481/0496: loss=29.798830032348633\n",
+      "epoch 001/005, batch 002/002, step 0001/0496: loss=39.485069274902344\n",
+      "epoch 001/005, batch 002/002, step 0002/0496: loss=39.97723388671875\n",
+      "epoch 001/005, batch 002/002, step 0003/0496: loss=38.477256774902344\n",
+      "epoch 002/005, batch 001/002, step 0001/0496: loss=21.981830596923828\n",
+      "epoch 002/005, batch 001/002, step 0129/0496: loss=18.73786735534668\n",
+      "epoch 002/005, batch 001/002, step 0257/0496: loss=15.319025039672852\n",
+      "epoch 002/005, batch 001/002, step 0385/0496: loss=14.001985549926758\n",
+      "epoch 003/005, batch 001/002, step 0001/0496: loss=9.053110122680664\n",
+      "epoch 003/005, batch 001/002, step 0129/0496: loss=8.859332084655762\n",
+      "epoch 003/005, batch 001/002, step 0257/0496: loss=6.927613258361816\n",
+      "epoch 003/005, batch 001/002, step 0385/0496: loss=12.725240707397461\n",
+      "epoch 004/005, batch 001/002, step 0001/0496: loss=5.395730018615723\n",
+      "epoch 004/005, batch 001/002, step 0129/0496: loss=4.600362300872803\n",
+      "epoch 004/005, batch 001/002, step 0257/0496: loss=5.964381694793701\n",
+      "epoch 004/005, batch 001/002, step 0385/0496: loss=7.421572208404541\n",
+      "epoch 005/005, batch 001/002, step 0001/0496: loss=5.333580493927002\n",
+      "epoch 005/005, batch 001/002, step 0129/0496: loss=4.6271843910217285\n",
+      "epoch 005/005, batch 001/002, step 0257/0496: loss=4.5750274658203125\n",
+      "epoch 005/005, batch 001/002, step 0385/0496: loss=7.874950408935547\n",
+      "Training done, saved NN\n"
+     ]
+    }
+   ],
    "source": [
-    "current_lr = lr\n",
     "steps = 0\n",
-    "for j in range(epochs):  # training\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",
-    "    current_lr = lr_schedule(j, current_lr) if adapt_lr else lr\n",
     "    for ib in range(dataset.numBatches):   \n",
     "        for i in range(dataset.numSteps): \n",
-    "            batch = getData(dataset, consecutive_frames=msteps) # should be dataset.getData\n",
-    "            re_nr = batch[2] # Reynolds numbers\n",
-    "            source = source.copied_with(density=batch[0][0], velocity=batch[1][0])\n",
-    "            reference = [ reference[k].copied_with(density=batch[0][k+1], velocity=batch[1][k+1]) for k in range(msteps) ]\n",
     "\n",
-    "            my_feed_dict = { source_in: source, Re_in: re_nr, lr_in: current_lr }\n",
-    "            my_feed_dict.update(zip(reference_in, reference))\n",
-    "            _, l2 = sess.run([train_step, loss], my_feed_dict)\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",
-    "\n",
-    "            if (j==0 and i<3) or (j==0 and ib==0 and i%31==0) or (ib==0 and i%124==0):\n",
-    "              print('epoch {:03d}/{:03d}, batch {:03d}/{:03d}, step {:04d}/{:04d}: loss={}'.format( j+1, epochs, ib+1, dataset.numBatches, i+1, dataset.numSteps, l2 ))\n",
+    "            if (j==0 and i<3) or (j==0 and ib==0 and i%32==0) or (j>0 and ib==0 and i%128==0): # 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",
@@ -846,7 +933,7 @@
     "    if j%10==9: network.save('./nn_epoch{:04d}.h5'.format(j+1))\n",
     "\n",
     "# all done! save final version\n",
-    "network.save('./final.h5')\n"
+    "network.save('./nn_final.h5'); print(\"Training done, saved NN\")\n"
    ]
   },
   {
@@ -855,9 +942,7 @@
     "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 resulting solver fares on new inputs.\n",
-    "\n",
-    "Note that 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",
+    "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",
@@ -874,105 +959,94 @@
     "\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 can 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 (denoted as `sourcesim` below). The second version, `prediction`, repeatedly computes the source solver plus the learned correction, and advances this state in the solver.\n",
+    "We can 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",
-    "A subtle but important point: we still have to use the normalization from the original training data set here, i.e., the `dataset.dataStats['std']` values. Below we'll create a new test data set with it's own mean and standard deviation, and so the trained NN never saw this data before. It was trained with the data in `dataset` above, and hence we have to use the constants from there to make sure the network receives values that it can relate to the data it was trained with."
+    "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), so that we can later on run the unmodified simulator and the DL-powered one on these cases.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "id": "RumKebW_05xp"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded test data, 4 training sims\n"
+     ]
+    }
+   ],
    "source": [
-    "# similar to msteps from before\n",
-    "evalsteps=1\n",
+    "fname_test = 'pbdl-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://ge.in.tum.de/download/2020-solver-in-the-loop/\"+fname_test, fname_test)\n",
     "\n",
-    "sourcesim_in = phi.tf.util.placeholder_like(source) # reuse source from above\n",
-    "sourcesim, prediction, correction = [], [], []\n",
-    "\n",
-    "for i in range(evalsteps):\n",
-    "    sourcesim += [\n",
-    "        KarmanFlow().step(\n",
-    "            sourcesim_in if i==0 else prediction[-1],\n",
-    "            re=Re_in,\n",
-    "            res=source_res[-1], \n",
-    "            velBCy=velBCy,\n",
-    "            velBCyMask=velBCyMask\n",
-    "        )\n",
-    "    ]\n",
-    "\n",
-    "    # important: use normalization from _original_ dataset here, not the testdata below\n",
-    "    correction += [\n",
-    "        to_staggered(\n",
-    "            network(\n",
-    "                to_keras(sourcesim[-1], Re_in)/[\n",
-    "                    *(dataset.dataStats['std'][1]),  # velocity\n",
-    "                    dataset.dataStats['ext.std'][0]  # Re\n",
-    "                ]\n",
-    "            ) * dataset.dataStats['std'][1],\n",
-    "            box=source.velocity.box\n",
-    "        )\n",
-    "    ]\n",
-    "\n",
-    "    prediction += [sourcesim[-1].copied_with(velocity=sourcesim[-1].velocity + correction[-1])]\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": {},
    "source": [
-    "Now we also need a set of new data to apply the learned solver to. Below, we'll download a new set of Reynolds numbers (in between the ones used for training), so that we can later on run the unmodified simulator and the DL-powered one on these cases.\n",
+    "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",
-    "We're creating a new dataset object `dataset_test` here, which organizes the data. We're simply using the first batch of the unshuffled dataset, though."
+    "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": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded test data, 3 training sims\n",
-      "dict_keys(['karman-fdt-hires-testset-reduced/sim_000000', 'karman-fdt-hires-testset-reduced/sim_000001', 'karman-fdt-hires-testset-reduced/sim_000002'])\n",
-      "Reynolds numbers in test data set: [240000.0, 960000.0, 3840000.0]\n"
+      "Reynolds numbers in test data set: (120000.0, 480000.0, 1920000.0, 7680000.0) along batch\n"
      ]
     }
    ],
    "source": [
-    "if not os.path.isfile('sol-data-karman-2d-test.pickle'):\n",
-    "  import urllib.request\n",
-    "  url=\"https://ge.in.tum.de/download/2020-solver-in-the-loop/sol-data-karman-2d-test.pickle\"\n",
-    "  print(\"Downloading test data (38MB), this can take a moment the first time...\")\n",
-    "  urllib.request.urlretrieve(url, 'sol-data-karman-2d-test.pickle')\n",
+    "dataset_test = Dataset( data_preloaded=data_test_preloaded, is_testset=True, num_frames=simsteps, num_sims=4, batch_size=4 )\n",
     "\n",
-    "with open('sol-data-karman-2d-test.pickle', 'rb') as f: data_test_preloaded = pickle.load(f)\n",
-    "print(\"Loaded test data, {} training sims\".format(len(data_test_preloaded)) )\n",
-    "\n",
-    "print(format(data_test_preloaded.keys()))\n",
-    "dataset_test = Dataset( data_preloaded=data_test_preloaded, is_testset=True, num_frames=simsteps, num_sims=3, batch_size=batch_size )\n",
-    "\n",
-    "# we only need 1 batch to start with\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=msteps) \n",
-    "re_nr = batch[2] # Reynolds numbers\n",
-    "source_test = source.copied_with(density=batch[0][0], velocity=batch[1][0])\n",
-    "reference = [ reference[k].copied_with(density=batch[0][k+1], velocity=batch[1][k+1]) for k in range(msteps) ]\n",
+    "batch = getData(dataset_test, consecutive_frames=0) \n",
     "\n",
-    "print(\"Reynolds numbers in test data set: \"+format(re_nr))"
+    "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": {},
    "source": [
-    "Now we can evaluate the _source_ simulation, and the _prediction_ of our learned hybrid solver for 120 steps:"
+    "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 can use `phi.math.stack()` to combine two fields of appropriate size as a staggered grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "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": {},
+   "source": [
+    "Now we can first run the _source_ simulation for 120 steps as baseline:"
    ]
   },
   {
@@ -989,12 +1063,18 @@
     }
    ],
    "source": [
-    "steps_source = [source_test]\n",
+    "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",
-    "    my_feed_dict = { sourcesim_in: steps_source[-1], Re_in: re_nr, lr_in: current_lr }\n",
-    "    sourcesim_out = sess.run([sourcesim[0]], my_feed_dict) \n",
-    "    steps_source.append( sourcesim_out[0] )\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)))"
    ]
@@ -1003,7 +1083,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The way the evaluation is implemented above, we can only evaluate a proper source simulation (i.e. one that is not modified by the NN) for `evalsteps=1`."
+    "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"
    ]
   },
   {
@@ -1020,34 +1100,54 @@
     }
    ],
    "source": [
-    "steps_pred = [source_test]\n",
-    "assert(evalsteps==1) # for other settings, deactivate the source evaluation below\n",
-    "\n",
+    "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",
-    "    my_feed_dict = { sourcesim_in: steps_pred[-1], Re_in: re_nr, lr_in: current_lr }\n",
-    "    prediction_out = sess.run([prediction[0]], my_feed_dict)  # w corr\n",
-    "    steps_pred.append( prediction_out[0] )\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",
-    "print(\"Steps with hybrid solver \"+format(len(steps_pred)))"
+    "    steps_hybrid.append( [source_dens_test,source_vel_test+correction] )\n",
+    "    \n",
+    "print(\"Steps with hybrid solver \"+format(len(steps_hybrid)))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's 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."
+    "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": 54,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE for source: 0.13630695641040802 , and hybrid: 0.07546938955783844\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 1152x360 with 6 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -1058,24 +1158,78 @@
    ],
    "source": [
     "import pylab\n",
-    "b = 1 # batch index for the following comparisons\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": {},
+   "source": [
+    "Due to the complexity of the training, the performance can vary, but typically the overall MAE is almost 80% 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, usually around 300%. \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": 33,
+   "metadata": {},
+   "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",
+    "\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",
-    "fig, axes = pylab.subplots(1, 6, figsize=(16, 5))\n",
-    "for i in range(1,7):\n",
-    "  v = steps_source[i*20].velocity.data[0].data[b,:,:,0] \n",
-    "  axes[i-1].imshow( v , origin='lower', cmap='magma')\n",
-    "  axes[i-1].set_title(f\" Source simulation t={i*20} \")\n",
     "pylab.tight_layout()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1152x360 with 6 Axes>"
       ]
@@ -1088,10 +1242,10 @@
    ],
    "source": [
     "fig, axes = pylab.subplots(1, 6, figsize=(16, 5))\n",
-    "for i in range(1,7):\n",
-    "  v = steps_pred[i*20].velocity.data[0].data[b,:,:,0] \n",
-    "  axes[i-1].imshow( v , origin='lower', cmap='magma')\n",
-    "  axes[i-1].set_title(f\" Hybrid solver t={i*20} \")\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()"
    ]
   },
@@ -1099,21 +1253,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "They both start out with the same initial state at $t=0$ (a 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",
+    "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 has learned to revert the overly strong dissipation of the solver. However, it also produces some visible axis-aligned structures, especially near the sides of the domain. (This could be alleviated with improved training setups, e.g., more look-ahead, and a larger model.)\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 can also compare and quantify how the models do 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:"
+    "We can 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": 56,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1008x360 with 4 Axes>"
       ]
@@ -1126,27 +1280,25 @@
    ],
    "source": [
     "index = 50 # time step index\n",
-    "\n",
-    "ref = [ math.concat( dataset_test.dataPreloaded[ dataset_test.dataSims[b] ][ j ][1] , axis=0) for j in range(121) ]\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",
-    "v = ref[index][:,:,0] \n",
-    "axes[0].imshow( v , origin='lower', cmap='magma')\n",
+    "axes[0].imshow( vx_ref , origin='lower', cmap='magma')\n",
     "axes[0].set_title(f\" Reference \")\n",
     "\n",
-    "v = steps_source[index].velocity.data[0].data[b,:,:,0] \n",
-    "axes[1].imshow( v , origin='lower', cmap='magma')\n",
+    "axes[1].imshow( vx_src , origin='lower', cmap='magma')\n",
     "axes[1].set_title(f\" Source \")\n",
     "\n",
-    "v = steps_pred[index].velocity.data[0].data[b,:,:,0] \n",
-    "axes[2].imshow( v , origin='lower', cmap='magma')\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 = ref[index][:,1:-2,0] - steps_source[index].velocity.data[0].data[b,:,1:-1,0] \n",
-    "err_pred   = ref[index][:,1:-2,0] - steps_pred[index].velocity.data[0].data[b,:,1:-1,0] \n",
-    "v = np.concatenate([err_source,err_pred], axis=1)\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='magma')\n",
     "axes[3].set_title(f\" Errors: Source & Learned\")\n",
     "\n",
@@ -1157,63 +1309,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This shows very clearly how the pure source simulation in the middle deviates from the reference on the left. Apart from the slight vertical streaks, the learned version stays much closer to the reference solution. \n",
+    "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 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.\n",
-    "\n",
-    "Next, we can quantify these observations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE for source: 0.05912385508418083 , and hybrid: 0.0402643121778965\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": [
-    "errors_source, errors_pred = [], []\n",
-    "for index in range(100):\n",
-    "  vs = ref[index][:,1:-2,0] - steps_source[index].velocity.data[0].data[b,:,1:-1,0] \n",
-    "  vp = ref[index][:,1:-2,0] - steps_pred[index].velocity.data[0].data[b,:,1:-1,0] \n",
-    "  errors_source.append(np.mean(np.abs(vs))) \n",
-    "  errors_pred.append(np.mean(np.abs(vp)))\n",
-    "  #print(\"Errors for step \"+format(index)+\" source: \"+format(np.mean(np.abs(vs))) +\" , hybrid: \"+format(np.mean(np.abs(vp))))\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)) )\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": {},
    "source": [
-    "The overall mean absolute error (MAE) is almost 50% larger for the regular simulation. The graph below shows this behavior over time: the error of the source version is clearly larger than the errors of the hybrid simulator.\n",
-    "\n",
-    "This concludes our evaluation. Note that the improved behavior of the hybrid solver is 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."
+    "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."
    ]
   },
   {
@@ -1226,11 +1331,12 @@
     "\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",
-    "* Use the external github code to generate new test data, and run your model on these cases. You'll see that a reduced training error not always directly correlates with an improved test performance.\n",
-    "\n",
     "* Turn off the differentiable physics training (by setting `msteps=1`), and compare it with the DP version.\n",
     "\n",
-    "* Likewise, train a model 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."
+    "* 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"
    ]
   }
  ],
@@ -1260,4 +1366,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
\ No newline at end of file
+}