168 lines
5.4 KiB
Markdown
168 lines
5.4 KiB
Markdown
Welcome ...
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============================
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Welcome to the Physics-based Deep Learning Book 👋
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**TL;DR**: This document targets
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a veriety of combinations of physical simulations with deep learning.
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As much as possible, the algorithms will come with hands-on code examples to quickly get started.
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Beyond standard _supervised_ learning from data, we'll look at loss constraints, and
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more tightly coupled learning algorithms with differentiable simulations.
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```{figure} resources/teaser.png
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---
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height: 220px
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name: pbdl-teaser
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---
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Some examples ... preview teaser ...
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```
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% Teaser, simple version:
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% 
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As a _sneak preview_, in the next chapters we'll show:
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- How to train networks to infer fluid flow solutions around shapes like airfoils in one go, i.e., without needing a simulator.
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- 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.
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- 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.
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This _book_, where book stands for a collection of text, equations, images and code examples,
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is maintained by the
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[TUM Physics-based Simulation Group](https://ge.in.tum.de). Feel free to contact us via
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[old fashioned email](mailto:i15ge@cs.tum.edu) if you have any comments.
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If you find mistakes, please also let us know! We're aware that this document is far from perfect,
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and we're eager to improve it. Thanks in advance!
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This collection of materials is a living document, and will grow and change over time.
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Feel free to contribute 😀
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[TUM Physics-based Simulation Group](https://ge.in.tum.de).
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We also maintain a [link collection](https://github.com/thunil/Physics-Based-Deep-Learning) with recent research papers.
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```{admonition} Code, executable, right here, right now
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:class: tip
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We focus on jupyter notebooks, a key advantage of which is that all code examples
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can be executed _on the spot_, out of a browser. You can modify things and
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immediately see what happens -- give it a try...
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<br><br>
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Oh, and it's great because it's [literate programming](https://en.wikipedia.org/wiki/Literate_programming).
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```
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## Specifically
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To be a bit more specific, _physics_ is a huge field, we can't cover everything...
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```{note}
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For now our focus is:
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- field-based simulations , less Lagrangian
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- simulations, not experiments
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- combination with _deep learning_ (plenty of other interesting ML techniques)
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```
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---
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## Thanks!
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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 🙏
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- Mr. X
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- Ms. y
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- ...
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% some markdown tests follow ...
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---
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a b c
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```{admonition} My title2
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:class: seealso
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See also... Test link: {doc}`supervised`
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```
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✅ Do this , ❌ Don't do this
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% ----------------
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---
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## Planned content
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Loose collection of notes and TODOs:
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General physics & dl , intro & textual overview
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- Intro phys loss example, notebook patrick
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Supervised? Airfoils? Liwei, simple example? app: optimization, shape opt w surrogates
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- AIAA supervised learning , idp_weissenov/201019-upd-arxiv-v2/ {cite}`thuerey2020deepFlowPred`
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skepticism? , started colab -> https://colab.research.google.com/drive/11KUe5Ybuprd7_qmNTe1nvQVUz3W6gRUo
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torch version 1.7 [upd from Liwei?]
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vs. PINNs [alt.: neural ODEs , PDE net?] , all using GD (optional, PINNs could use BFGS)
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[PINNs], phiflow example -> convert to colab
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- PINNs -> are unsupervised a la tompson; all DL NNs are "supervised" during learning, unsup just means not precomputed and goes through function
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- add image | NN | <> | Loss | , backprop; (bring back every section, add variants for other methods?)
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- discuss CG solver, tompson as basic ''unsupervisedd'' example?
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Diff phys, start with overview of idea: gradients via autodiff, then run GD
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(TODO include squared func Patrick?)
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- illustrate and discuss gradients -> mult. for chain rule; (later: more general PG chain w func composition)
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- Differentiable Physics (w/o network) , {cite}`holl2019pdecontrol`
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-> phiflow colab notebook good start, but needs updates (see above Jan2)
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- SOL_201019-finals_Solver-in-the-Loop-Main-final.pdf , {cite}`um2020sol`
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numerical errors, how to include in jupyter / colab?
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- ICLR_190925-ICLR-final_1d8cf33bb3c8825e798f087d6cd35f2c7c062fd4.pdf alias
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PDE control, control focused
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https://github.com/holl-/PDE-Control -> update to new version?
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beyond GD: re-cap newton & co
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Phys grad (PGs) as fundamental improvement, PNAS case; add more complex one?
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PG update of poisson eq? see PNAS-template-main.tex.bak01-poissonUpdate , explicitly lists GD and PG updates
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- PGa 2020 Sept, content: ML & opt
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Gradients.pdf, -> overleaf-physgrad/
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- PGb 201002-beforeVac, content: v1,v2,old - more PG focused
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-> general intro versions
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TODO, for version 2.x add:
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time series, sequence prediction?] {cite}`wiewel2019lss,bkim2019deep,wiewel2020lsssubdiv`
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include DeepFluids variant?
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[BAYES , prob?]
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include results Jakob
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[unstruct / lagrangian] {cite}`prantl2019tranquil,ummenhofer2019contconv`
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include ContConv / Lukas
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---
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_Misc jupyter book TODOs_
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- Fix latex PDF output
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- How to include links to papers in the bibtex references?
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