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+Welcome ... 
+============================
+
+Welcome to the Physics-based Deep Learning Book 👋
+
+**TL;DR**: This document targets 
+a veriety of combinations of physical simulations with deep learning.
+As much as possible, the algorithms will come with hands-on code examples to quickly get started.
+Beyond standard _supervised_ learning from data, we'll look at loss constraints, and 
+more tightly coupled learning algorithms with differentiable simulations.
+
+As a _sneak preview_, in the next chapters we'll show:
+
+- How to train networks to infer fluid flow solutions around shapes like airfoils in one go, i.e., without needing a simulator.
+
+- We'll show how to use model equations as residual to train networks that represent solutions, and how to improve upon this behavior by using differentiable simulations.
+
+- Even more tightly coupling a full _rough_ simulator for control problems is another topic. E.g., we'll demonstrate how to circumvent the convergence problems of standard reinforcement learning techniques by leveraging simulators in the training loop.
+
+This _book_, where book stands for a collection of text, equations, images and code examples,
+is maintained by the
+[TUM Physics-based Simulation Group](https://ge.in.tum.de). Feel free to contact us via
+[old fashioned email](mailto:i15ge@cs.tum.edu) if you have any comments. 
+If you find mistakes, please also let us know! We're aware that this document is far from perfect,
+and we're eager to improve it. Thanks in advance!
+
+TODO, add teaser pic
+
+## Thanks!
+
+The contents of the following files would not have been possible without the help of many people. Here's an alphabetical list. Big kudos to everyone 🙏
+
+- Mr. X
+- Ms. y
+- ...
+
+
+
+% ----------------
+
+
+===
+
+## Planned content, loose collection of notes and TODOs:
+
+General physics & dl , intro & textual overview
+    more general intro: https://github.com/thunil/Physics-Based-Deep-Learning
+
+Supervised? Airfoils? Liwei, simple example? app: optimization, shape opt w surrogates
+
+-    AIAA supervised learning , idp_weissenov/201019-upd-arxiv-v2/  {cite}`thuerey2020deepFlowPred`
+    skepticism? , started colab -> https://colab.research.google.com/drive/11KUe5Ybuprd7_qmNTe1nvQVUz3W6gRUo
+    torch version 1.7 [upd from Liwei?]
+
+vs. PINNs [alt.: neural ODEs , PDE net?] , all using GD (optional, PINNs could use BFGS)
+    [PINNs], phiflow example -> convert to colab
+
+-    PINNs -> are unsupervised a la tompson; all DL NNs are "supervised" during learning, unsup just means not precomputed and goes through function
+
+-    add image | NN | <> | Loss | , backprop; (bring back every section, add variants for other methods?)
+
+-    discuss CG solver, tompson as basic ''unsupervisedd'' example?
+
+Diff phys, start with overview of idea: gradients via autodiff, then run GD
+        (TODO include squared func Patrick?)
+
+-    Differentiable Physics (w/o network) , {cite}`holl2019pdecontrol` 
+        -> phiflow colab notebook good start, but needs updates (see above Jan2)
+    illustrate and discuss gradients -> mult. for chain rule; (later: more general PG chain w func composition)
+
+-    SOL_201019-finals_Solver-in-the-Loop-Main-final.pdf , {cite}`um2020sol` 
+        numerical errors, how to include in jupyter / colab?
+
+-    ICLR_190925-ICLR-final_1d8cf33bb3c8825e798f087d6cd35f2c7c062fd4.pdf alias
+        PDE control, control focused
+        https://github.com/holl-/PDE-Control    -> update to new version?
+
+beyond GD: re-cap newton & co
+
+Phys grad (PGs) as fundamental improvement, PNAS case; add more complex one?
+        PG update of poisson eq? see PNAS-template-main.tex.bak01-poissonUpdate , explicitly lists GD and PG updates
+
+PGa 2020 Sept, content: ML & opt
+    Gradients.pdf, -> overleaf-physgrad/ 
+
+PGb 201002-beforeVac, content: v1,v2,old - more PG focused
+    -> general intro versions
+
+[MISSING, time series, sequence prediction?] {cite}`wiewel2019lss,bkim2019deep,wiewel2020lsssubdiv`
+[BAYES , prob?]
+[unstruct / lagrangian] {cite}`prantl2019tranquil,ummenhofer2019contconv`
+Outlook