diff --git a/_toc.yml b/_toc.yml
index 11437e8..5077b05 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -37,6 +37,16 @@
   - file: diffphys-control.ipynb
   - file: diffphys-outlook.md
 
+- part: Reinforcement Learning
+  chapters:
+  - file: reinflearn-intro.md
+  - file: reinflearn-code.ipynb
+  
+- part: PBDL and Uncertainty 
+  chapters:
+  - file: bayesian-intro.md
+  - file: bayesian-code.ipynb
+  
 - part: Physical Gradients
   chapters:
   - file: physgrad.md
@@ -44,11 +54,6 @@
   - file: physgrad-nn.md
   - file: physgrad-discuss.md
 
-- part: PBDL and Uncertainty 
-  chapters:
-  - file: bayesian-intro.md
-  - file: bayesian-code.md
-  
 - part: Fast Forward Topics
   chapters:
   - file: others-intro.md
diff --git a/bayesian-intro.md b/bayesian-intro.md
index 5b72759..4a42649 100644
--- a/bayesian-intro.md
+++ b/bayesian-intro.md
@@ -1,7 +1,7 @@
 Introduction to Posterior Inference
 =======================
 
-We have to keep in mind that for all measurements, models, and discretizations we have uncertainties. In the former, this typically appears in the form of measurements errors, model equations usually encompass only parts of a system we're interested in, and for numerical simulations we inherently introduce discretization errors. So a very important question to ask here is how sure we can be sure that an answer we obtain is the correct one. From a statistics viewpoint, we'd like to know the probability distribution for the posterior, i.e., the outcomes.
+We should keep in mind that for all measurements, models, and discretizations we have uncertainties. For the former, this typically appears in the form of measurements errors, while model equations usually encompass only parts of a system we're interested in, and for numerical simulations we inherently introduce discretization errors. So a very important question to ask here is how sure we can be sure that an answer we obtain is the correct one. From a statistics viewpoint, we'd like to know the probability distribution for the posterior, i.e., the different outcomes that are possible.
 
 This admittedly becomes even more difficult in the context of machine learning:
 we're typically facing the task of approximating complex and unknown functions.
@@ -10,22 +10,36 @@ yields a _maximum likelihood estimation_ (MLE) for the parameters of the network
 However, this MLE viewpoint does not take any of the uncertainties mentioned above into account:
 for DL training, we likewise have a numerical optimization, and hence an inherent
 approximation error and uncertainty regarding the learned representation.
-Ideally, we could change our learning problem such that we could do _posterior inference_,
+Ideally, we should reformulate our learning problem such that it enables _posterior inference_,
 i.e. learn to produce the full output distribution. However, this turns out to be an
 extremely difficult task.
 
 This where so called _Bayesian neural network_ (BNN) approaches come into play. They 
-make posterior inference possible by making assumptions about the probability 
-distributions of individual parameters of the network. Nonetheless, the task
+make a form of posterior inference possible by making assumptions about the probability 
+distributions of individual parameters of the network. With a distribution for the
+parameters we can evaluate the network multiple times to obtain different versions
+of the output, and in this way sample the distribution of the output.
+
+Nonetheless, the task
 remains very challenging. Training a BNN is typically significantly more difficult
 than training a regular NN. However, this should come as no surprise, as we're trying to 
-learn something fundamentally different in this case: a full probability distribution 
-instead of a point estimate.
+learn something fundamentally different here: a full probability distribution 
+instead of a point estimate. (All previous chapters "just" dealt with
+learning such point estimates.)
 
 ![Divider](resources/divider5.jpg)
 
+## Introduction to Bayesian Neural Networks
+
+
+**TODO, integrate Maximilians intro section here**
+...
+
+
 ## A practical example
 
-first example here with airfoils, extension from {doc}`supervised-airfoils`
-
+As a first real example for posterior inference with BNNs, let's revisit the
+case of turbulent flows around airfoils, from {doc}`supervised-airfoils`. However,
+in contrast to the point estimate learned in this section, we'll now aim for
+learning the full posterior.
 
diff --git a/diffphys.md b/diffphys.md
index ffd8a0a..fa4d796 100644
--- a/diffphys.md
+++ b/diffphys.md
@@ -62,7 +62,7 @@ given model parameter, with which the NN should not interact.
 Naturally, it can vary within the solution manifold that we're interested in, 
 but $\nu$ will not be the output of a NN representation. If this is the case, we can omit
 providing $\partial \mathcal P_i / \partial \nu$ in our solver. However, the following learning process
-natuarlly transfers to including $\nu$ as a degree of freedom.
+naturally transfers to including $\nu$ as a degree of freedom.
 
 ## Jacobians
 
@@ -152,7 +152,7 @@ we could leverage the $O(n)$ runtime of multigrid solvers for matrix inversion.
 The flipside of this approach is, that it requires some understanding of the problem at hand, 
 and of the numerical methods. Also, a given solver might not provide gradient calculations out of the box.
 Thus, we want to employ DL for model equations that we don't have a proper grasp of, it might not be a good
-idea to direclty go for learning via a DP approach. However, if we don't really understand our model, we probably
+idea to directly go for learning via a DP approach. However, if we don't really understand our model, we probably
 should go back to studying it a bit more anyway...
 
 Also, in practice we can be _greedy_ with the derivative operators, and only 
@@ -191,7 +191,7 @@ Note that to simplify things, we assume that $\mathbf{u}$ is only a function in
 i.e. constant over time. We'll bring back the time evolution of $\mathbf{u}$ later on.
 %
 Let's denote this re-formulation as $\mathcal P$. It maps a state of $d(t)$ into a 
-new state at an evoled time, i.e.:
+new state at an evolved time, i.e.:
 
 $$
     d(t+\Delta t) = \mathcal P ( ~ d(t), \mathbf{u}, t+\Delta t) 
@@ -289,7 +289,7 @@ be preferable to actually constructing $A$.
 As a slightly more complex example let's consider Poisson's equation $\nabla^2 a = b$, where
 $a$ is the quantity of interest, and $b$ is given. 
 This is a very fundamental elliptic PDE that is important for 
-a variety of physical problems, from electrostatics to graviational fields. It also arises 
+a variety of physical problems, from electrostatics to gravitational fields. It also arises 
 in the context of fluids, where $a$ takes the role of a scalar pressure field in the fluid, and
 the right hand side $b$ is given by the divergence of the fluid velocity $\mathbf{u}$.
 
diff --git a/others-intro.md b/others-intro.md
index 6ec1ce0..e37feb9 100644
--- a/others-intro.md
+++ b/others-intro.md
@@ -12,8 +12,16 @@ More specifically, we will look at:
   This typically replaces a numerical solver, and we can make use of special techniques from the DL area that target time series.
 
 * Generative models are likewise an own topic in DL, and here especially generative adversarial networks were shown to be powerful tools. They also represent a highly interesting training approach involving to separate NNs.
+{cite}`xie2018tempoGan`
 
 * Meshless methods and unstructured meshes are an important topic for classical simulations. Here, we'll look at a specific Lagrangian method that employs learning in the context of dynamic, particle-based representations.
+{cite}`prantl2019tranquil`
+{cite}`ummenhofer2019contconv`
 
-* Finally, metrics to reboustly assess the quality of similarity of measurements and results are a central topic for all numerical methods, no matter whether they employ learning or not. In the last section we will look at how DL can be used to learn specialized and improved metrics.
+https://github.com/intel-isl/DeepLagrangianFluids
 
+* Finally, metrics to robustly assess the quality of similarity of measurements and results are a central topic for all numerical methods, no matter whether they employ learning or not. In the last section we will look at how DL can be used to learn specialized and improved metrics.
+
+{cite}`kohl2020lsim`
+
+{cite}`um2020sol`
diff --git a/others-timeseries.md b/others-timeseries.md
index ae10366..6881a0e 100644
--- a/others-timeseries.md
+++ b/others-timeseries.md
@@ -1,7 +1,53 @@
 Model Reduction and Time Series
 =======================
 
-model reduction? separate
+An inherent challenge for many practical PDE solvers is the large dimensionality of the problem.
+Our model $\mathcal{P}$ is typically discretized with $\mathcal{O}(n^3)$ samples for a 3 dimensional 
+problem (with $n$ denoting the number of samples along one axis), 
+and for time-dependent phenomena we additionally have a discretization along
+time. The latter typically scales in accordance to the spatial dimensions, giving an
+overall number of samples on the order of $\mathcal{O}(n^4)$. Not surprisingly, 
+the workload in these situations quickly explodes for larger $n$ (and for practical high-fidelity applications we want $n$ to be as large as possible).
+
+One popular way to reduce the complexity is to map a spatial state of our system $\mathbf{s_t} \in \mathbb{R}^{n^3}$
+into a much lower dimensional state $\mathbf{c_t} \in \mathbb{R}^{m}$, with $m \ll n^3$. Within this latent space,
+we estimate the evolution of our system by inferring a new state $\mathbf{c_{t+1}}$, which we then decode to obtain $\mathbf{s_{t+1}}$. In order for this to work, it's crucial that we can choose $m$ large enough that it captures all important structures in our solution manifold, and that the time prediction of $\mathbf{c_{t+1}}$ can be computed efficiently, such that we obtain a gain in performance despite the additional encoding and decoding steps. In practice, due to the explosion in terms of unknowns for regular simulations (the $\mathcal{O}(n^3)$ above) coupled a super-linear complexity for computing a new state, working with the latent space points $\mathbf{c}$ quickly pays off for small $m$.
+
+However, it's crucial that encoder and decoder do a good job at reducing the dimensionality of the problem. This is a very good task for DL approaches. Furthermore, we then need a time evolution of the latent space states $\mathbf{c}$, and for most practical model equations, we cannot find closed form solutions to evolve $\mathbf{c}$. Hence, this likewise poses a very good problem for learning methods. To summarize, we're facing to challenges: learning a good spatial encoding and decoding, together with learning an accurate time evolution.
+Below, we will describe an approach to solve this problem following Wiewel et al.
+{cite}`wiewel2019lss` & {cite}`wiewel2020lsssubdiv`, which in turn employs 
+the encoder/decoder of Kim et al. {cite}`bkim2019deep`.
+
+
+```{figure} resources/timeseries-lsp-overview.jpg
+---
+height: 200px
+name: timeseries-lsp-overview
+---
+For time series predictions with ROMs, we encode the state of our system with an encoder $f_e$, predict 
+the time evolution with $f_t$, and then decode the full spatial information with a decoder $f_d$.
+```
+
+
+## Reduced Order Models 
+
+Reducing the order of computational models, often called _reduced order modeling_ (ROM) or _model reduction_,
+as a classic topic in the computational field. Traditional techniques often employ techniques such as principal component analysis to arrive at a basis for a chosen space of solution. However, being linear by construction, these approaches have inherent limitations when representing complex, non-linear solution manifolds. And in practice, all "interesting" solutions are highly non-linear.
+
+
+$\text{arg min}_{\theta} | f_d( f_e(x;\theta_e) ;\theta_d) - x |_2^2$
+
+$f_e: \mathbb{R}^{n^3} \rightarrow \mathbb{R}^{m}$
+
+$f_d: \mathbb{R}^{m} \rightarrow \mathbb{R}^{n^3}$
+
+
+separable model
+
+
+
+## Time Series
+
 
 ...
 
diff --git a/physgrad.md b/physgrad.md
index 4b8c62f..a7983d6 100644
--- a/physgrad.md
+++ b/physgrad.md
@@ -1,6 +1,8 @@
 Physical Gradients
 =======================
 
+**Note, this chapter is very preliminary - probably not for the first version of the book**
+
 The next chapter will dive deeper into state-of-the-art-research, and aim for an even tighter
 integration of physics and learning.
 The approaches explained previously all integrate physical models into deep learning algorithms,
diff --git a/reinflearn-code.md b/reinflearn-code.md
deleted file mode 100644
index aa14885..0000000
--- a/reinflearn-code.md
+++ /dev/null
@@ -1,797 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "PDE-Control-RL.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Aml7ksJPtCmf"
-      },
-      "source": [
-        "# Inverse Problems with Reinforcement Learning"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AHETNWlVtyWr"
-      },
-      "source": [
-        "This notebook trains reinforcement learning agents controlling Burgers' equation, a nonlinear PDE. The approach uses the reinforcement learning framework [stable_baselines3](https://github.com/DLR-RM/stable-baselines3) and the differentiable PDE solver [Φ<sub>Flow</sub>](https://github.com/tum-pbs/PhiFlow). [PPO](https://arxiv.org/abs/1707.06347v2) was chosen as reinforcement learning algorithm.\n",
-        "\n",
-        "Additionally, a supervised control force estimator is trained as a performance baseline. This method was introduced by Holl et al. [\\(2020\\)](https://ge.in.tum.de/publications/2020-iclr-holl/)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "2EDqLS_xz9B8"
-      },
-      "source": [
-        "!pip install stable-baselines3 phiflow==1.5.1\n",
-        "!git clone https://github.com/Sh0cktr4p/PDE-Control-RL.git\n",
-        "!git clone https://github.com/holl-/PDE-Control.git"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A9t4odMH6pl1"
-      },
-      "source": [
-        "# Training"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "UI1_mMnNQXrN"
-      },
-      "source": [
-        "import sys; sys.path.append('PDE-Control/src'); sys.path.append('PDE-Control-RL/src')\n",
-        "import time\n",
-        "import csv\n",
-        "import os\n",
-        "import shutil\n",
-        "from tensorboard.backend.event_processing.event_accumulator import EventAccumulator\n",
-        "from phi.flow import *\n",
-        "import burgers_plots as bplt\n",
-        "import matplotlib.pyplot as plt\n",
-        "from envs.burgers_util import GaussianClash, GaussianForce"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mCUbc-sovPME"
-      },
-      "source": [
-        "## Data Generation"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wSELidjsvRyd"
-      },
-      "source": [
-        "At first we generate a dataset to train the CFE model on and evaluate the performance of both approaches during and after training."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "lUENHywEUVsu"
-      },
-      "source": [
-        "domain = Domain([32], box=box[0:1])     # Defines the size of the fields\n",
-        "viscosity = 0.003\n",
-        "step_count = 32                         # Trajectory length\n",
-        "dt = 0.03\n",
-        "diffusion_substeps = 1\n",
-        "\n",
-        "data_path = 'forced-burgers-clash'\n",
-        "scene_count = 1000\n",
-        "batch_size = 100\n",
-        "\n",
-        "train_range = range(200, 1000)\n",
-        "val_range = range(100, 200)\n",
-        "test_range = range(0, 100)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "vEMdDJjAUeUv"
-      },
-      "source": [
-        "for batch_index in range(scene_count // batch_size):\n",
-        "    scene = Scene.create(data_path, count=batch_size)\n",
-        "    print(scene)\n",
-        "    world = World()\n",
-        "    u0 = BurgersVelocity(\n",
-        "        domain, \n",
-        "        velocity=GaussianClash(batch_size), \n",
-        "        viscosity=viscosity, \n",
-        "        batch_size=batch_size, \n",
-        "        name='burgers'\n",
-        "    )\n",
-        "    u = world.add(u0, physics=Burgers(diffusion_substeps=diffusion_substeps))\n",
-        "    force = world.add(FieldEffect(GaussianForce(batch_size), ['velocity']))\n",
-        "    scene.write(world.state, frame=0)\n",
-        "    for frame in range(1, step_count + 1):\n",
-        "        world.step(dt=dt)\n",
-        "        scene.write(world.state, frame=frame)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "plZUZD_av3YH"
-      },
-      "source": [
-        "## Reinforcement Learning Training"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "ZrczbfDzUgim"
-      },
-      "source": [
-        "from experiment import BurgersTraining"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Agc9EVUeUoY9"
-      },
-      "source": [
-        "n_envs = 10                         # On how many environments to train in parallel, load balancing\n",
-        "final_reward_factor = step_count    # How hard to punish the agent for not reaching the goal if that were the case\n",
-        "steps_per_rollout = step_count * 10 # How many steps to collect per environment between agent updates\n",
-        "n_epochs = 10                       # How many epochs to perform during agent update\n",
-        "rl_learning_rate = 1e-4             # Learning rate for agent updates\n",
-        "rl_batch_size = 128                 # Batch size for agent updates"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "U4FKqSjwv9jR"
-      },
-      "source": [
-        "To start training, we create a trainer object which manages the environment and the agent internally. Additionally, a directory for storing models, logs, and hyperparameters is created. This way, training can be continued at any later point using the same configuration. If the model folder specified in exp_name already exists, the agent within is loaded. Otherwise, a new agent is created.\n",
-        "\n",
-        "As default, a already trained agent stored at PDE-Control-RL/networks/rl-models/bench is loaded. To generate a new model, replace the specified path with another."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FjB_0vNKVCxe",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "df262ee8-c3a3-4479-c750-4d9fdba40784"
-      },
-      "source": [
-        "rl_trainer = BurgersTraining(\n",
-        "    path='PDE-Control-RL/networks/rl-models/bench', # Replace this to train a new model\n",
-        "    domain=domain,\n",
-        "    viscosity=viscosity,\n",
-        "    step_count=step_count,\n",
-        "    dt=dt,\n",
-        "    diffusion_substeps=diffusion_substeps,\n",
-        "    n_envs=n_envs,\n",
-        "    final_reward_factor=final_reward_factor,\n",
-        "    steps_per_rollout=steps_per_rollout,\n",
-        "    n_epochs=n_epochs,\n",
-        "    learning_rate=rl_learning_rate,\n",
-        "    batch_size=rl_batch_size,\n",
-        "    data_path=data_path,\n",
-        "    val_range=val_range,\n",
-        "    test_range=test_range,\n",
-        ")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Loading existing agent from PDE-Control-RL/networks/rl-models/bench/agent.zip\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "skE_zAdGwkM2"
-      },
-      "source": [
-        "The following cell opens tensorboard inside the notebook to display the progress of the training. If a new model was created at a different location, please change the path to the location at which you stored your model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "DM8bVThNVF9Y"
-      },
-      "source": [
-        "%load_ext tensorboard\n",
-        "%tensorboard --logdir PDE-Control-RL/networks/rl-models/bench/tensorboard-log"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nY5uq750wzsK"
-      },
-      "source": [
-        "Now we are set up to start training the agent. The next cell might take multiple hours to execute, depending on the number of rollouts (around 6h for 1000 iterations)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "laqpvrc7VxcW"
-      },
-      "source": [
-        "rl_trainer.train(n_rollouts=1000, save_freq=50)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7WlqEvsOL7Rt"
-      },
-      "source": [
-        "Now let us take a quick glance about what the results look like.\n",
-        "Here also an example for a trajectory after around 3600 training iterations:\n",
-        "![burgers_notebook.png](data:image/png;base64,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)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Y05aa5BjMVFZ"
-      },
-      "source": [
-        "rl_frames, _, _ = rl_trainer.infer_test_set_frames()\n",
-        "\n",
-        "index_in_set = 0    # Change this to display a reconstruction of another scene\n",
-        "\n",
-        "bplt.burgers_figure('Reinforcement Learning')\n",
-        "for frame in range(0, step_count + 1):\n",
-        "    plt.plot(rl_frames[frame][index_in_set,:], color=bplt.gradient_color(frame, step_count+1), linewidth=0.8)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "u47Yboxx2CGV"
-      },
-      "source": [
-        "## Control Force Estimator Training"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2E_sFqgo2SiU"
-      },
-      "source": [
-        "To classify the results of the reinforcement learning method, they are compared to a supervised control force estimator approach using differentiable physics loss. This comparison seems reasonable, as both approaches work by optimizing through trial and error.\n",
-        "\n",
-        "The CFE approach has access to the gradient data provided by the differentiable solver, making it possible to trace the loss over multiple timesteps and enabling the model to comprehend long term effects of generated forces better.\n",
-        "\n",
-        "The reinforcement learning approach on the other hand uses a dedicated value estimator network (critic) to predict the sum of rewards generated from a certain state. These are then used to update a policy network (actor) which, analogously to the control force estimator network, predicts the forces to control the simulation. The reinforcement learning algorithm is not limited by training set size like the CFE approach, as new training samples are generated on policy. However, this also introduces additional simulation overhead during training, which can increase the time needed for convergence. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "U2hqdeJb2GLJ"
-      },
-      "source": [
-        "from control.pde.burgers import BurgersPDE\n",
-        "from control.control_training import ControlTraining\n",
-        "from control.sequences import StaggeredSequence"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PLcFuRBz0-Yf"
-      },
-      "source": [
-        "The cell below sets up a model for training or to load an existing model checkpoint."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "5s6-hSZp5CGb"
-      },
-      "source": [
-        "cfe_app = ControlTraining(\n",
-        "    step_count,\n",
-        "    BurgersPDE(domain, viscosity, dt),\n",
-        "    datapath=data_path,\n",
-        "    val_range=val_range,\n",
-        "    train_range=train_range,\n",
-        "    trace_to_channel=lambda trace: 'burgers_velocity',\n",
-        "    obs_loss_frames=[],\n",
-        "    trainable_networks=['CFE'],\n",
-        "    sequence_class=StaggeredSequence,\n",
-        "    batch_size=100,\n",
-        "    view_size=20,\n",
-        "    learning_rate=1e-3,\n",
-        "    learning_rate_half_life=1000,\n",
-        "    dt=dt\n",
-        ").prepare()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3ReXUkzI1L3t"
-      },
-      "source": [
-        "The cell below executes the model training. Please specify a number of iterations for the algorithm to run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "blHHLaVS5jHA"
-      },
-      "source": [
-        "cfe_training_eval_data = []\n",
-        "\n",
-        "start_time = time.time()\n",
-        "\n",
-        "for epoch in range(200):\n",
-        "    cfe_app.progress()\n",
-        "    # Evaluate validation set at regular intervals to track learning progress\n",
-        "    # Size of intervals determined by RL epoch count per iteration for accurate comparison\n",
-        "    if epoch % n_epochs == 0:\n",
-        "        f = cfe_app.infer_scalars(val_range)['Total Force'] / dt\n",
-        "        cfe_training_eval_data.append((time.time() - start_time, epoch, f))"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "31B72FBR1pXr"
-      },
-      "source": [
-        "The following cells stores the trained model and the validation performance with respect to iterations and wall time."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "kzfckDMc8O__"
-      },
-      "source": [
-        "cfe_store_path = 'networks/cfe-models/bench'\n",
-        "if not os.path.exists(cfe_store_path):\n",
-        "    os.makedirs(cfe_store_path)\n",
-        "\n",
-        "# store training progress information\n",
-        "with open(os.path.join(cfe_store_path, 'val_forces.csv'), 'at') as log_file:\n",
-        "    logger = csv.DictWriter(log_file, ('time', 'epoch', 'forces'))\n",
-        "    logger.writeheader()\n",
-        "    for (t, e, f) in cfe_training_eval_data:\n",
-        "        logger.writerow({'time': t, 'epoch': e, 'forces': f})\n",
-        "\n",
-        "cfe_checkpoint = cfe_app.save_model()\n",
-        "shutil.move(cfe_checkpoint, cfe_store_path)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "r4r6sOh87B-1"
-      },
-      "source": [
-        "Alternatively, run the cell below can be run to load an existing network model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "gHEAxxjv-vDL"
-      },
-      "source": [
-        "cfe_path = 'PDE-Control-RL/networks/cfe-models/bench/checkpoint_00020000/'\n",
-        "networks_to_load = ['OP2', 'OP4', 'OP8', 'OP16', 'OP32']\n",
-        "\n",
-        "cfe_app.load_checkpoints({net: cfe_path for net in networks_to_load})"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "V8inOSSE0OMf"
-      },
-      "source": [
-        "Run the cell below to see how the algorithm performs. Here is an example of a model trained for 2000 iterations:\n",
-        "![burgers_notebook2.png](data:image/png;base64,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)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "SjYY0PXp1VDT"
-      },
-      "source": [
-        "cfe_frames = cfe_app.infer_all_frames(test_range)\n",
-        "\n",
-        "index_in_set = 1    # Change this to display a reconstruction of another scene\n",
-        "\n",
-        "bplt.burgers_figure('Supervised Control Force Estimator')\n",
-        "for frame in range(0, step_count + 1):\n",
-        "    plt.plot(cfe_frames[frame].burgers.velocity.data[index_in_set,:,0], color=bplt.gradient_color(frame, step_count+1), linewidth=0.8)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fr-_uYpQ_nHn"
-      },
-      "source": [
-        "# Comparison"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gA63vV4d2yko"
-      },
-      "source": [
-        "Next, the results of both methods are compared in terms of visual quality of the resulting trajectories as well as the generated forces. The latter provides insight about the performance of either approaches as both aspire to minimize this metric during training"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "QuoxjQf8UVuF"
-      },
-      "source": [
-        "import utils\n",
-        "import pandas as pd"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JsUf1411_-xd"
-      },
-      "source": [
-        "## Trajectory Comparison"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Yh-xD2cG7d9A"
-      },
-      "source": [
-        "To compare the resulting trajectories, we generate trajectories from the test set with each method. Also, we collect the ground truth simulations and the natural evolution of the test set fields."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EP8S7UC5_vQD"
-      },
-      "source": [
-        "rl_frames, gt_frames, unc_frames = rl_trainer.infer_test_set_frames()\n",
-        "\n",
-        "cfe_frames = cfe_app.infer_all_frames(test_range)\n",
-        "cfe_frames = [s.burgers.velocity.data for s in cfe_frames]\n",
-        "\n",
-        "frames = {\n",
-        "    (0, 0): ('Ground Truth', gt_frames),\n",
-        "    (0, 1): ('Uncontrolled', unc_frames),\n",
-        "    (1, 0): ('Reinforcement Learning', rl_frames),\n",
-        "    (1, 1): ('Supervised Control Force Estimator', cfe_frames),\n",
-        "}"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "p6DJ3J6jAkQ5"
-      },
-      "source": [
-        "index_in_set = 0    # Specifies which sample of the test set should be displayed\n",
-        "\n",
-        "def plot(axs, xy, title, field):\n",
-        "    axs[xy].set_ylim(-2, 2)\n",
-        "    axs[xy].set_xlabel('x')\n",
-        "    axs[xy].set_ylabel('u(x)')\n",
-        "    axs[xy].set_title(title)\n",
-        "\n",
-        "    label = 'Initial state in dark red, final state in dark blue'\n",
-        "\n",
-        "    for step_idx in range(0, step_count + 1):\n",
-        "        color = bplt.gradient_color(step_idx, step_count+1)\n",
-        "        axs[xy].plot(\n",
-        "            field[step_idx][index_in_set].squeeze(), \n",
-        "            color=color, \n",
-        "            linewidth=0.8, \n",
-        "            label=label\n",
-        "        )\n",
-        "        label = None\n",
-        "\n",
-        "    axs[xy].legend()\n",
-        "\n",
-        "fig, axs = plt.subplots(2, 2, figsize=(12.8, 9.6))\n",
-        "\n",
-        "for xy in frames:\n",
-        "    plot(axs, xy, *frames[xy])"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rPjprKl-DWAX"
-      },
-      "source": [
-        "## Forces Comparison"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZsksKs4e4QJA"
-      },
-      "source": [
-        "Next, we compute the forces the approaches have generated for the test set trajectories."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "me4rb-_qCXgC"
-      },
-      "source": [
-        "gt_forces = utils.infer_forces_sum_from_frames(\n",
-        "    gt_frames, domain, diffusion_substeps, viscosity, dt\n",
-        ")\n",
-        "cfe_forces = utils.infer_forces_sum_from_frames(\n",
-        "    cfe_frames, domain, diffusion_substeps, viscosity, dt\n",
-        ")\n",
-        "rl_forces = rl_trainer.infer_test_set_forces()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "B75xKFuw4414"
-      },
-      "source": [
-        "In the following, the forces generated by both methods are compared to the ground truth of the respective sample. Samples placed above the blue line denote stronger forces in the used deep learning approach than in the ground truth and vice versa."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "zMK29jgWDUB_"
-      },
-      "source": [
-        "plt.figure(figsize=(12.8, 9.6))\n",
-        "plt.scatter(gt_forces, cfe_forces, label='CFE')\n",
-        "plt.scatter(gt_forces, rl_forces, label='RL')\n",
-        "plt.plot([x * 100 for x in range(15)], [x * 100 for x in range(15)], label='Same forces as original')\n",
-        "plt.xlabel('ground truth')\n",
-        "plt.xlim(0, 1500)\n",
-        "plt.ylim(0, 1500)\n",
-        "plt.ylabel('reconstruction')\n",
-        "plt.grid()\n",
-        "plt.legend()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ema5wsX25gS6"
-      },
-      "source": [
-        "Next, the two deep learning methods are compared directly. Samples above the line denote higher forces by the control force estimator, samples below higher forces for the reinforcement learning agent."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "gEgGuyhcDkPK"
-      },
-      "source": [
-        "plt.figure(figsize=(12.8, 9.6))\n",
-        "plt.scatter(rl_forces, cfe_forces)\n",
-        "plt.xlabel('Reinforcement Learning')\n",
-        "plt.ylabel('Control Force Estimator')\n",
-        "plt.plot([x * 100 for x in range(15)], [x * 100 for x in range(15)], label='Same forces cfe rl')\n",
-        "plt.xlim(0, 1500)\n",
-        "plt.ylim(0, 1500)\n",
-        "plt.grid()\n",
-        "plt.legend()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7JqW7Cca6HUJ"
-      },
-      "source": [
-        "The following plot displays the performance of all reinforcement learning, control force estimator and ground truth with respect to individual samples."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "0vqJmQ3FDnKA"
-      },
-      "source": [
-        "w=0.25\n",
-        "plot_count=20\n",
-        "plt.figure(figsize=(12.8, 9.6))\n",
-        "plt.bar(\n",
-        "    [i - w for i in range(plot_count)], \n",
-        "    rl_forces[:plot_count], \n",
-        "    width=w, \n",
-        "    align='center', \n",
-        "    label='RL'\n",
-        ")\n",
-        "plt.bar(\n",
-        "    [i + w for i in range(plot_count)], \n",
-        "    cfe_forces[:plot_count], \n",
-        "    width=w, \n",
-        "    align='center', \n",
-        "    label='CFE'\n",
-        ")\n",
-        "plt.bar(\n",
-        "    [i for i in range(plot_count)], \n",
-        "    gt_forces[:plot_count], \n",
-        "    width=w, \n",
-        "    align='center', \n",
-        "    label='GT'\n",
-        ")\n",
-        "plt.xlabel('Scenes')\n",
-        "plt.xticks(range(plot_count))\n",
-        "plt.ylabel('Forces')\n",
-        "plt.legend()\n",
-        "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9Ee3Us_hD9nR"
-      },
-      "source": [
-        "## Training Progress Comparison"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DGBlUpQ271Ww"
-      },
-      "source": [
-        "This cell explores the training progress of both methods with respect to iterations and wall time."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "i1ecSuakEAYp"
-      },
-      "source": [
-        "def get_cfe_val_set_forces(experiment_path):\n",
-        "    path = os.path.join(experiment_path, 'val_forces.csv')\n",
-        "    table = pd.read_csv(path)\n",
-        "    return list(table['time']), list(table['epoch']), list(table['forces'])\n",
-        "\n",
-        "rl_w_times, rl_step_nums, rl_val_forces = rl_trainer.get_val_set_forces_data()\n",
-        "cfe_w_times, cfe_epochs, cfe_val_forces = get_cfe_val_set_forces('PDE-Control-RL/networks/cfe-models/bench')\n",
-        "\n",
-        "fig, axs = plt.subplots(2, 1, figsize=(12.8, 9.6))\n",
-        "\n",
-        "axs[0].plot(np.array(rl_step_nums), rl_val_forces, label='RL')\n",
-        "axs[0].plot(np.array(cfe_epochs), cfe_val_forces, label='CFE')\n",
-        "axs[0].set_xlabel('Epochs')\n",
-        "axs[0].set_ylabel('Forces')\n",
-        "axs[0].set_ylim(0, 1500)\n",
-        "axs[0].grid()\n",
-        "axs[0].legend()\n",
-        "\n",
-        "axs[1].plot(np.array(rl_w_times) / 3600, rl_val_forces, label='RL')\n",
-        "axs[1].plot(np.array(cfe_w_times) / 3600, cfe_val_forces, label='CFE')\n",
-        "axs[1].set_xlabel('Wall time (hours)')\n",
-        "axs[1].set_ylabel('Forces')\n",
-        "axs[1].set_ylim(0, 1500)\n",
-        "axs[1].grid()\n",
-        "axs[1].legend()"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "ih2a2rackAPs"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}