diff --git a/diffphys-code-control.ipynb b/diffphys-code-control.ipynb
index cdce4b9..95d2280 100644
--- a/diffphys-code-control.ipynb
+++ b/diffphys-code-control.ipynb
@@ -15,7 +15,8 @@
     "Below we will run a very challenging test case as a representative of these inverse problems: we will aim for computing a high dimensional control function that exerts forces over the full course of an incompressible fluid simulation in order to reach a desired goal state for a passively advected marker in the fluid. This means we only have very indirect constraints to be fulfilled (a single state at the end of a sequence), and a large number of degrees of freedom (the control force function is a space-time function with the same degrees of freedom as the flow field itself).\n",
     "\n",
     "The _long-term_ nature of the control is one of the aspects which makes this a tough inverse problem: any changes to the state of the physical system can lead to large change later on in time, and hence a controller needs to anticipate how the system will behave when it is influenced. This means an NN also needs to learn how the underlying physics evolve and change, and this is exactly where the gradients from the DP training come in to guide the learning task towards solution that can reach the goal.\n",
-    "[[run in colab]](https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/diffphys-code-control.ipynb)\n"
+    "\n",
+    "[_Warning_: This code is a very \"classic\" one by now, and requires quite a few legacy APIs that are not supported on colab anymore (most importantly Python3.6 with TF1.14 and phiflow 1.4.1). Hence it's recommended to run this example in a local conda environment rather than colab.]\n"
    ]
   },
   {
@@ -169,7 +170,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1224x360 with 10 Axes>"
       ]
@@ -460,17 +461,6 @@
     "Note that we have not actually set up a simulation for the training, as the $\\mathrm{CFE}$ network only infers forces between pairs of states. \n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "kQZLbOolI4-2"
-   },
-   "outputs": [],
-   "source": [
-    "# [TODO, show preview of CFE only?]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -652,7 +642,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1008x432 with 30 Axes>"
       ]
diff --git a/diffphys-code-ns.ipynb b/diffphys-code-ns.ipynb
index 8dd6d7b..a2d7f0b 100644
--- a/diffphys-code-ns.ipynb
+++ b/diffphys-code-ns.ipynb
@@ -116,7 +116,7 @@
     {
      "data": {
       "text/plain": [
-       "(inflow_loc\u1d47=4, x\u02e2=32, y\u02e2=40)"
+       "(inflow_locᵇ=4, xˢ=32, yˢ=40)"
       ]
      },
      "execution_count": 2,
@@ -172,7 +172,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 720x432 with 4 Axes>"
       ]
@@ -253,7 +253,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Velocity dimensions: (inflow_loc\u1d47=4, x\u02e2=32, y\u02e2=40, vector\u1d9c=x,y)\n"
+      "Velocity dimensions: (inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᶜ=x,y)\n"
      ]
     }
    ],
@@ -279,9 +279,9 @@
     "id": "pjqeVedM_trm"
    },
    "source": [
-    "Phiflow's `field.functional_gradient()` function is the central function to compute gradients. Next, we'll use it to obtain the gradient with respect to the initial velocity. Since the velocity is the second argument of the `simulate()` function, we pass `wrt=[1]`. (Phiflow also has a `field.spatial_gradient` function which instead computes derivatives of tensors along spatial dimensions, like `x,y`.)\n",
+    "Phiflow's `field.gradient()` function is the central function to compute gradients. Next, we'll use it to obtain the gradient with respect to the initial velocity. Since the velocity is the second argument of the `simulate()` function, we pass `wrt=[1]`. (Phiflow also has a `field.spatial_gradient` function which instead computes derivatives of tensors along spatial dimensions, like `x,y`.)\n",
     "\n",
-    "`functional_gradient` generates a gradient function. As a demonstration, the next cell evaluates the gradient once with the initial states for smoke and velocity. The last statement prints a summary of a part of the resulting gradient tensor.\n"
+    "`gradient` generates a gradient function. As a demonstration, the next cell evaluates the gradient once with the initial states for smoke and velocity. The last statement prints a summary of a part of the resulting gradient tensor.\n"
    ]
   },
   {
@@ -299,13 +299,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Some gradient info: StaggeredGrid[(inflow_loc\u1d47=4, x\u02e2=32, y\u02e2=40, vector\u1d9c=x,y), size=\u001b[94m(x=32, y=40)\u001b[0m \u001b[93mint64\u001b[0m, extrapolation=\u001b[94m0\u001b[0m]\n",
-      "\u001b[92m(x\u02e2=31, y\u02e2=40)\u001b[0m \u001b[94m2.61e-08 \u00b1 8.5e-01\u001b[0m \u001b[37m(-2e+01...1e+01)\u001b[0m\n"
+      "Some gradient info: StaggeredGrid[(inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᶜ=x,y), size=\u001b[94m(x=32, y=40)\u001b[0m \u001b[93mint64\u001b[0m, extrapolation=\u001b[94m0\u001b[0m]\n",
+      "\u001b[92m(xˢ=31, yˢ=40)\u001b[0m \u001b[94m2.61e-08 ± 8.5e-01\u001b[0m \u001b[37m(-2e+01...1e+01)\u001b[0m\n"
      ]
     }
    ],
    "source": [
-    "sim_grad = field.functional_gradient(simulate, wrt=[1], get_output=False)\n",
+    "sim_grad = field.gradient(simulate, wrt=[1], get_output=False)\n",
     "(velocity_grad,) = sim_grad(initial_smoke, initial_velocity)\n",
     "\n",
     "print(\"Some gradient info: \" + format(velocity_grad))\n",
@@ -342,7 +342,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 864x360 with 5 Axes>"
       ]
@@ -383,7 +383,7 @@
     "\n",
     "The following cell implements a simple steepest gradient descent optimization: it re-evaluates the gradient function, and iterates several times to optimize $\\mathbf{u}_0$ with a learning rate (step size) `LR`.\n",
     "\n",
-    "`field.functional_gradient` has a parameter `get_output` that determines whether the original results of the function (`simulate()` in our case) are returned, or only the gradient. As it's interesting to track how the loss evolves over the course of the iterations, let's redefine the gradient function with `get_output=True`. \n"
+    "`field.gradient` has a parameter `get_output` that determines whether the original results of the function (`simulate()` in our case) are returned, or only the gradient. As it's interesting to track how the loss evolves over the course of the iterations, let's redefine the gradient function with `get_output=True`. \n"
    ]
   },
   {
@@ -416,7 +416,7 @@
     }
    ],
    "source": [
-    "sim_grad_wloss = field.functional_gradient(simulate, wrt=[1], get_output=True) # if we need outputs...\n",
+    "sim_grad_wloss = field.gradient(simulate, wrt=[1], get_output=True) # if we need outputs...\n",
     "\n",
     "LR = 1e-03 \n",
     "for optim_step in range(80):    \n",
@@ -450,7 +450,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 720x288 with 6 Axes>"
       ]
@@ -484,7 +484,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 720x288 with 6 Axes>"
       ]
@@ -531,7 +531,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 720x432 with 4 Axes>"
       ]
@@ -581,7 +581,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 720x432 with 12 Axes>"
       ]
@@ -624,9 +624,9 @@
     "## Conclusions\n",
     "\n",
     "This example illustrated how the differentiable physics approach can easily be extended towards significantly more \n",
-    "complex PDEs. Above, we've optimized for a mini-batch of 20 steps of a full Navier-Stokes solver.\n",
+    "complex PDEs. Above, we've optimized for a mini-batch of 20 steps of a full Navier-Stokes solver. 😄 \n",
     "\n",
-    "This is a powerful basis to bring NNs into the picture. As you might have noticed, our degrees of freedom were still a regular grid, and we've jointly solved a single inverse problem. There were three cases to solve as a mini-batch, of course, but nonetheless the setup still represents a direct optimization. Thus, in line with the PINN example of {doc}`physicalloss-code` we've not really dealt with a _machine learning_ task here. However, DP training allows for a range of flexible combinations with NNs that will be the topic of the next chapters.\n"
+    "This is a powerful basis to bring NNs into the picture. Above, the degrees of freedom were still a regular grid, and we've jointly solved a single inverse problem. There were three cases to solve as a mini-batch, of course, but nonetheless the setup still represents a direct _optimization_. Thus, in line with the PINN example of {doc}`physicalloss-code` we've not really dealt with a _machine learning_ task here. However, DP training allows for a range of flexible combinations with NNs that will be the topic of the next chapters.\n"
    ]
   },
   {
diff --git a/diffphys-code-sol.ipynb b/diffphys-code-sol.ipynb
index e6cc3ed..cee26b1 100644
--- a/diffphys-code-sol.ipynb
+++ b/diffphys-code-sol.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "# Reducing Numerical Errors with Neural Operators\n",
     "\n",
-    "In this example we will target numerical errors that arise in the discretization of a continuous PDE $\\mathcal P^*$, i.e. when we formulate $\\mathcal P$. This approach will demonstrate that, despite the lack of closed-form descriptions, discretization errors often are functions with regular and repeating structures and, thus, can be learned by a neural operator. Once trained, the neural network (NN) can be evaluated locally to improve the solution of a PDE-solver, i.e., to reduce its numerical error. The resulting method is a hybrid one: it will always perform (a coarse) PDE solve, and then improve it at runtime with corrections inferred by an NN.\n",
+    "In this example we will target numerical errors that arise in the discretization of a continuous PDE $\\mathcal P^*$, i.e. when we formulate $\\mathcal P$. This approach will demonstrate that, despite the lack of closed-form descriptions, discretization errors often are functions with regular and repeating structures and, thus, can be learned by a discretized neural operator. Once trained, the neural network (NN) can be evaluated locally to improve the solution of a PDE-solver, i.e., to reduce its numerical error. The resulting method is a hybrid one: it will always perform (a coarse) PDE solve, and then improve it at runtime with corrections inferred by an NN.\n",
     "\n",
     "\n",
     "Pretty much all numerical methods contain some form of iterative process: repeated updates over time for explicit solvers, or within a single update step for implicit or steady-state solvers.\n",
@@ -79,10 +79,8 @@
     "\\newcommand{\\vr}[1]{\\mathbf{r}_{#1}}\n",
     "\\loss ( \\mathcal{P}_{s}( \\corr (\\mathcal{T} \\vr{t}) ) , \\mathcal{T} \\vr{t+1}) < \\loss ( \\mathcal{P}_{s}( \\mathcal{T} \\vr{t} ), \\mathcal{T} \\vr{t+1})$.\n",
     "\n",
-    "The correction operator  \n",
-    "$\\newcommand{\\vcN}{\\mathbf{s}} \\newcommand{\\corr}{\\mathcal{C}} \\corr (\\vcN | \\theta)$\n",
-    "is represented as a deep neural network with weights $\\theta$\n",
-    "and receives the state $\\mathbf{s}$ to infer an additive correction field with the same dimension.\n",
+    "The correction operator $\\newcommand{\\vcN}{\\mathbf{s}} \\newcommand{\\corr}{\\mathcal{C}} \\corr (\\vcN | \\theta)$ is represented \n",
+    "as a deep neural network with weights $\\theta$ and receives the state $\\mathbf{s}$ to infer an additive correction field with the same dimension.\n",
     "To distinguish the original states $\\mathbf{s}$ from the corrected ones, we'll denote the latter with an added tilde $\\tilde{\\mathbf{s}}$.\n",
     "The overall learning goal now becomes\n",
     "\n",
@@ -136,8 +134,9 @@
    "source": [
     "try:\n",
     "    import google.colab  # to ensure that we are inside colab\n",
-    "    !pip install --upgrade --quiet phiflow==3.2\n",
+    "    !pip install --upgrade --quiet phiflow==3.3\n",
     "    #!pip install --upgrade --quiet git+https://github.com/tum-pbs/PhiFlow@develop\n",
+    "    \n",
     "    # for pbdl-dataset:\n",
     "    !pip install --upgrade --quiet git+https://github.com/tum-pbs/pbdl-dataset\n",
     "\n",
diff --git a/diffphys-discuss.md b/diffphys-discuss.md
index b987579..f05edb2 100644
--- a/diffphys-discuss.md
+++ b/diffphys-discuss.md
@@ -11,13 +11,19 @@ What is primarily exciting in this context are the implications that arise from
 Most importantly, training via differentiable physics allows us to seamlessly bring the two fields together:
 we can obtain _hybrid_ methods, that use the best numerical methods that we have at our disposal for the simulation itself, as well as for the training process. We can then use the trained model to improve forward or backward solves. Thus, in the end, we have a solver that combines a _traditional_ solver and a _learned_ component that in combination can improve the capabilities of numerical methods.
 
-## Interaction
+## Reducing data shift via interaction
 
-One key aspect that is important for these hybrids to work well is to let the NN _interact_ with the PDE solver at training time. Differentiable simulations allow a trained model to "explore and experience" the physical environment, and receive directed feedback regarding its interactions throughout the solver iterations. This combination nicely fits into the broader context of machine learning as _differentiable programming_. 
+One key aspect that is important for these hybrids to work well is to let the NN _interact_ with the PDE solver at training time. Differentiable simulations allow a trained model to "explore and experience" the physical environment, and receive directed feedback regarding its interactions throughout the solver iterations. 
+
+This addresses the classic **data shift** problem of machine learning: rather than relying on a _a-priori_ specified distribution for training the network, the training process generates new trajectories via unrolling on the fly, and computes training signals from them. This can be seen as an _a-posteriori_ approach, and makes the trained NN significantly more resilient to unseen inputs. As we'll evaluate in more detail in {doc}`probmodels-uncond`, it's actually hard to beat a good unrolling setup with other approaches.
+
+Note that the topic of _differentiable physics_ nicely fits into the broader context of machine learning as _differentiable programming_. 
 
 ## Generalization
 
-The hybrid approach also bears particular promise for simulators: it improves generalizing capabilities of the trained models by letting the PDE-solver handle large-scale _changes to the data distribution_ such that the learned model can focus on localized structures not captured by the discretization. While physical models generalize very well, learned models often specialize in data distributions seen at training time. This was, e.g., shown for the models reducing numerical errors of the previous chapter: the trained models can deal with solution manifolds with significant amounts of varying physical behavior, while simpler training variants quickly deteriorate over the course of recurrent time steps.
+The hybrid approach also bears particular promise for simulators: it improves generalizing capabilities of the trained models by letting the PDE-solver handle large-scale _changes to the data distribution_. This allows the learned model to focus on localized structures not captured by the discretization. While physical models generalize very well, learned models often specialize in data distributions seen at training time. Hence, this aspect benefits from the previous reduction of data shift, and effectively allows for even larger differences in terms of input distribution. If the NN is set up correctly, these can be handled by the classical solver in a hybrid approach.
+
+These benefits were, e.g., shown for the models reducing numerical errors of {doc}`diffphys-code-sol`: the trained models can deal with solution manifolds with significant amounts of varying physical behavior, while simpler training variants would deteriorate over the course of recurrent time steps.
 
 
 ![Divider](resources/divider5.jpg)
@@ -28,16 +34,17 @@ To summarize, the pros and cons of training NNs via DP:
 - Uses physical model and numerical methods for discretization.
 - Efficiency and accuracy of selected methods carries over to training.
 - Very tight coupling of physical models and NNs possible.
-- Improved generalization via solver interactions.
+- Improved resilience and generalization.
 
 ❌ Con: 
 - Not compatible with all simulators (need to provide gradients).
 - Require more heavy machinery (in terms of framework support) than previously discussed methods.
 
-_Outlook_: the last negative point (regarding heavy machinery) is bound to strongly improve given the current pace of software and API developments in the DL area. However, for now it's important to keep in mind that not every simulator is suitable for DP training out of the box. Hence, in this book we'll focus on examples using phiflow, which was designed for interfacing with deep learning frameworks. 
+_Outlook_: the last negative point (regarding heavy machinery) is strongly improving at the moment. Many existing simulators, e.g. the popular open source framework _OpenFoma_, as well as many commercial simulators are working on tight integrations with NNs. However, there's still plenty room for improvement, and in this book we're focusing on examples using phiflow, which was designed for interfacing with deep learning frameworks from ground up. 
 
-The training via differentiable physics (DP) allows us to integrate full numerical simulations into the training of deep neural networks.
-It is also a very generic approach that is applicable to a wide range of combinations of PDE-based models and deep learning. 
+The training via differentiable physics (DP) allows us to integrate full numerical simulations into the training of deep neural networks. 
+This effectively provides **hard constraints**, as the coupled solver can project and enforce constraints just like classical solvers would.
+It is a very generic approach that is applicable to a wide range of combinations of PDE-based models and deep learning. 
 
-In the next chapters, we will first compare DP training to model-free alternatives for control problems, and afterwards target the underlying learning process to obtain even better NN states.
+In the next chapters, we will first expand the scope of the learning tasks to incorporate uncertainties, i.e. to work with full distributions rather than single deterministic states and trajectories. Afterwards, we'll also compare DP training to reinforcement learning, and target the underlying learning process to obtain even better NN states.
 
diff --git a/diffphys-dpvspinn.md b/diffphys-dpvspinn.md
index 2859319..ae6bbc4 100644
--- a/diffphys-dpvspinn.md
+++ b/diffphys-dpvspinn.md
@@ -16,13 +16,13 @@ The DP version on the other hand inherently relies on a numerical solver that is
 
 The reliance on a suitable discretization requires some understanding and knowledge of the problem under consideration. A sub-optimal discretization can impede the learning process or, worst case, lead to diverging training runs. However, given the large body of theory and practical realizations of stable solvers for a wide variety of physical problems, this is typically not an unsurmountable obstacle.
 
-The PINN approaches on the other hand do not require an a-priori choice of a discretization, and as such seems to be "discretization-less". This, however, is only an advantage on first sight. As they yield solutions in a computer, they naturally _have_ to discretize the problem. They construct this discretization over the course of the training process, in a way that lies at the mercy of the underlying nonlinear optimization, and is not easily controllable from the outside. Thus, the resulting accuracy is determined by how well the training manages to estimate the complexity of the problem for realistic use cases, and how well the training data approximates the unknown regions of the solution.
+The PINN approaches on the other hand do not require an a-priori choice of a discretization, and as such seems to be "discretization-less". This, however, is only an advantage on first sight. By now, researchers are trying to "re-integrate" discretizations into PINN training. Generally, PINNs inevitably yield solutions in a computer and thus _have_ to discretize the problem. They construct this discretization over the course of the training process, in a way that lies at the mercy of the underlying nonlinear optimization, and is not easily controllable from the outside. Thus, the resulting accuracy is determined by how well the training manages to estimate the complexity of the problem for realistic use cases, and how well the training data approximates the unknown regions of the solution. 
 
 E.g., as demonstrated with the Burgers example, the PINN solutions typically have significant difficulties propagating information _backward_ in time. This is closely coupled to the efficiency of the method.
 
 ## Efficiency
 
-The PINN approaches typically perform a localized sampling and correction of the solutions, which means the corrections in the form of weight updates are likewise typically local. The fulfillment of boundary conditions in space and time can be correspondingly slow, leading to long training runs in practice.
+The PINN approach also results in fundamentally more difficult training tasks that causes convergence problems. PINNs typically perform a localized sampling and correction of the solutions, which means the corrections in the form of weight updates are likewise typically local. The fulfillment of boundary conditions in space and time can be correspondingly slow, leading to long training runs in practice.
 
 A well-chosen discretization of a DP approach can remedy this behavior, and provide an improved flow of gradient information. At the same time, the reliance on a computational grid means that solutions can be obtained very quickly. Given an interpolation scheme or a set of basis functions, the solution can be sampled at any point in space or time given a very local neighborhood of the computational grid. Worst case, this can lead to slight memory overheads, e.g., by repeatedly storing mostly constant values of a solution.
 
@@ -54,4 +54,4 @@ The following table summarizes these pros and cons of physics-informed (PI) and
 
 As a summary, both methods are definitely interesting, and have a lot of potential. There are numerous more complicated extensions and algorithmic modifications that change and improve on the various negative aspects we have discussed for both sides.
 
-However, as of this writing, the physics-informed (PI) approach has clear limitations when it comes to performance and compatibility with existing numerical methods. Thus, when knowledge of the problem at hand is available, which typically is the case when we choose a suitable PDE model to constrain the learning process, employing a differentiable physics solver can significantly improve the training process as well as the quality of the obtained solution. So, in the following we'll focus on DP variants, and illustrate their capabilities with more complex scenarios in the next chapters. First, we'll consider a case that very efficiently computes space-time gradients for a transient fluid simulations.
+However, as of this writing, the PINN approach has clear limitations when it comes to performance and compatibility with existing numerical methods. Thus, when knowledge of the problem at hand is available, which typically is the case when we choose a suitable PDE model to constrain the learning process, employing a differentiable physics solver to train Neural operators can significantly improve the training process as well as the quality of the obtained solution. So, in the following we'll focus on DP variants, and illustrate their capabilities with more complex scenarios in the next chapters. First, we'll consider a case that very efficiently computes space-time gradients for a transient fluid simulations.
diff --git a/diffphys-examples.md b/diffphys-examples.md
index 872e600..ab4d0bc 100644
--- a/diffphys-examples.md
+++ b/diffphys-examples.md
@@ -7,7 +7,26 @@ When using DP approaches for learning applications,
 there is a lot of flexibility w.r.t. the combination of DP and NN building blocks. 
 As some of the differences are subtle, the following section will go into more detail.
 We'll especially focus on solvers that repeat the PDE and NN evaluations multiple times,
-e.g., to compute multiple states of the physical system over time.
+e.g., to compute multiple states of the physical system over time. In classical numerics,
+this would be called an iterative time stepping method, while in the context of AI, it's
+an _autoregressive_ method.
+
+
+
+```{admonition} Hint: Correction vs Prediction
+:class: tip
+
+The problems that are best tackled with DP approaches are very fundamental. The combination of 
+a imperfect physical model and an _improvement term_ classically goes under many different names:
+_closure problems_ in fluid dynamics and turbulence, _homogenization_ or _coarse-graining_ 
+in material science, while it's called _parametrization_ in climate and weather.
+
+In the following, we'll generically denote all these tasks containing NN+solver as **correction** task, in contrast
+to pure **prediction** tasks for cases where no solver is involved at inference time.
+
+```
+
+
 
 To re-cap, here's the previous figure about combining NNs and DP operators. 
 In the figure these operators look like a loss term: they typically don't have weights,
@@ -22,7 +41,11 @@ The DP approach as described in the previous chapters. A network produces an inp
 ```
 
 This setup can be seen as the network receiving information about how it's output influences the outcome of the PDE solver. I.e., the gradient will provide information how to produce an NN output that minimizes the loss. 
-Similar to the previously described _physical losses_ (from {doc}`physicalloss`), this can mean upholding a conservation law.
+Similar to the previously described {doc}`physicalloss`, this can, e.g., mean upholding a conservation law or generally a PDE-based constraint over time.
+
+
+
+
 
 ## Switching the order 
 
@@ -36,15 +59,15 @@ name: diffphys-switch
 A PDE solver produces an output which is processed by an NN.
 ```
 
-In this case the PDE solver essentially represents an _on-the-fly_ data generator. That's not necessarily always useful: this setup could be replaced by a pre-computation of the same inputs, as the PDE solver is not influenced by the NN. Hence, there's no backpropagation through $\mathcal P$, and it could be replaced by a simple "loading" function. On the other hand, evaluating the PDE solver at training time with a randomized sampling of input parameters can lead to an excellent sampling of the data distribution of the input. If we have realistic ranges for how the inputs vary, this can improve the NN training. If implemented correctly, the solver can also alleviate the need to store and load large amounts of data, and instead produce them more quickly at training time, e.g., directly on a GPU. 
+In this case the PDE solver essentially represents an _on-the-fly_ data generator. That's not necessarily always useful: this setup could be replaced by a pre-computation of the same inputs, as the PDE solver is not influenced by the NN. Hence, there's no backpropagation through $\mathcal P$, and it could be replaced by a simple "loading" function. On the other hand, evaluating the PDE solver at training time with a randomized sampling of input parameters can lead to an excellent sampling of the data distribution of the input. If we have realistic ranges for how the inputs vary, this can improve the NN training. If implemented correctly, the solver can also alleviate the need to store and load large amounts of data, and instead produce them more quickly at training time, e.g., directly on a GPU. Recent methods explore this direction in the context of _Active Learning_.
 
-However, this version does not leverage the gradient information from a differentiable solver, which is why the following variant is much more interesting.
+However, this version does not leverage the gradient information from a differentiable solver, which is why the following variant is more interesting.
 
 ## Recurrent evaluation
 
-In general, there's no combination of NN layers and DP operators that is _forbidden_ (as long as their dimensions are compatible). One that makes particular sense is to "unroll" the iterations of a time stepping process of a simulator, and let the state of a system be influenced by an NN.
+A combination that makes particular sense is to **unroll** the iterations of a time stepping process of a simulator, and let the state of a system be influenced by an NN. (In general, there's no combination of NN layers and DP operators that is _forbidden_ (as long as their dimensions are compatible).)
 
-In this case we compute a (potentially very long) sequence of PDE solver steps in the forward pass. In-between these solver steps, an NN modifies the state of our system, which is then used to compute the next PDE solver step. During the backpropagation pass, we move backwards through all of these steps to evaluate contributions to the loss function (it can be evaluated in one or more places anywhere in the execution chain), and to backprop the gradient information through the DP and NN operators. This unrollment of solver iterations essentially gives feedback to the NN about how it's "actions" influence the state of the physical system and resulting loss. Here's a visual overview of this form of combination:
+In the case of unrolling, we compute a (potentially very long) sequence of PDE solver steps in the forward pass. In-between these solver steps, an NN modifies the state of our system, which is then used to compute the next PDE solver step. During the backpropagation pass, we move backwards through all of these steps to evaluate contributions to the loss function (it can be evaluated in one or more places anywhere in the execution chain), and to backprop the gradient information through the DP and NN operators. This unrollment of solver iterations essentially gives feedback to the NN about how it's "actions" influence the state of the physical system and resulting loss. Here's a visual overview of this form of combination:
 
 ```{figure} resources/diffphys-multistep.jpg
 ---
@@ -54,7 +77,7 @@ name: diffphys-mulitstep
 Time stepping with interleaved DP and NN operations for $k$ solver iterations. The dashed gray arrows indicate optional intermediate evaluations of loss terms (similar to the solid gray arrow for the last step $k$), and intermediate outputs of the NN are indicated with a tilde.
 ```
 
-Due to the iterative nature of this process, errors will start out very small, and then slowly increase exponentially over the course of iterations. Hence they are extremely difficult to detect in a single evaluation, e.g., with a simpler supervised training setup. Rather, it is crucial to provide feedback to the NN at training time how the errors evolve over course of the iterations. Additionally, a pre-computation of the states is not possible for such iterative cases, as the iterations depend on the state of the NN. Naturally, the NN state is unknown before training time and changes while being trained. Hence, a DP-based training is crucial in these recurrent settings to provide the NN with gradients about how its current state influences the solver iterations, and correspondingly, how the weights should be changed to better achieve the learning objectives.
+Due to the iterative nature of this process, errors will start out very small, and then (for modes with eigenvalues larger than one in the Jacobian) slowly increase exponentially over the course of iterations. Hence they are extremely difficult to detect in a single evaluation, e.g., with a simpler supervised training setup. Rather, it is crucial to provide feedback to the NN at training time how the errors evolve over course of the iterations. Additionally, a pre-computation of the states is not possible for such iterative cases, as the iterations depend on the state of the NN. Naturally, the NN state is unknown before training time and changes while being trained. This is the classic ML problem of **data shift**. Hence, a DP-based training is crucial in these recurrent settings to provide the NN with gradients about how its current state influences the solver iterations, and correspondingly, how the weights should be changed to better achieve the learning objectives.
 
 DP setups with many time steps can be difficult to train: the gradients need to backpropagate through the full chain of PDE solver evaluations and NN evaluations. Typically, each of them represents a non-linear and complex function. Hence for larger numbers of steps, the vanishing and exploding gradient problem can make training difficult. Some practical considerations for alleviating this will follow int {doc}`diffphys-code-sol`.
 
@@ -169,6 +192,9 @@ for training setups that tend to overfit. However, if possible, it is preferable
 actual solver in the training loop via a DP approach to give the network feedback about the time 
 evolution of the system.
 
+With the current state of affairs, generative modeling approaches (denoising diffusion or flow matching) or 
+provide a better founded approach for incorporating noise. We'll look into this topic in more detail in {doc}`probmodels-uncond`.
+
 ---
 
 ## Complex examples
diff --git a/diffphys.md b/diffphys.md
index ead5036..c3b9b24 100644
--- a/diffphys.md
+++ b/diffphys.md
@@ -6,14 +6,17 @@ methods and physical simulations we will target incorporating _differentiable
 numerical simulations_ into the learning process. In the following, we'll shorten
 these "differentiable numerical simulations of physical systems" to just "differentiable physics" (DP).
 
-The central goal of these methods is to use existing numerical solvers, and equip
+The central goal of these methods is to use existing numerical solvers
+to empower and improve AI systems.
+This requires equipping
 them with functionality to compute gradients with respect to their inputs.
 Once this is realized for all operators of a simulation, we can leverage 
 the autodiff functionality of DL frameworks with backpropagation to let gradient 
 information flow from a simulator into an NN and vice versa. This has numerous 
 advantages such as improved learning feedback and generalization, as we'll outline below.
 
-In contrast to physics-informed loss functions, it also enables handling more complex
+In contrast to the physics-informed loss functions of the previous chapter, 
+it also enables handling more complex
 solution manifolds instead of single inverse problems. 
 E.g., instead of using deep learning
 to solve single inverse problems as in the previous chapter, 
@@ -31,7 +34,7 @@ provide directions in the form of gradients to steer the learning process.
 
 ## Differentiable operators
 
-With the DP direction we build on existing numerical solvers. I.e., 
+With DP we build on _existing_ numerical solvers. I.e., 
 the approach is strongly relying on the algorithms developed in the larger field 
 of computational methods for a vast range of physical effects in our world.
 To start with, we need a continuous formulation as model for the physical effect that we'd like 
@@ -128,6 +131,7 @@ one by one.
 For the details of forward and reverse mode differentiation, please check out external materials such 
 as this [nice survey by Baydin et al.](https://arxiv.org/pdf/1502.05767.pdf).
 
+
 ## Learning via DP operators 
 
 Thus, once the operators of our simulator support computations of the Jacobian-vector 
@@ -209,7 +213,7 @@ Informally, we'd like to find a flow that deforms $d^{~0}$ through the PDE model
 The simplest way to express this goal is via an $L^2$ loss between the two states. So we want
 to minimize the loss function $L=|d(t^e) - d^{\text{target}}|^2$. 
 
-Note that as described here this inverse problem is a pure optimization task: there's no NN involved,
+Note that as described here, this inverse problem is a pure optimization task: there's no NN involved,
 and our goal is to obtain $\mathbf{u}$. We do not want to apply this velocity to other, unseen _test data_,
 as would be custom in a real learning task.