119 lines
4.8 KiB
Markdown
119 lines
4.8 KiB
Markdown
Welcome ...
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============================
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```{figure} resources/logo-xl.jpg
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---
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name: pbdl-logo-large
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---
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```
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Welcome to the _Physics-based Deep Learning Book_ (v0.2) 👋
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**TL;DR**:
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This document contains a practical and comprehensive introduction of everything
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related to deep learning in the context of physical simulations.
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As much as possible, all topics come with hands-on code examples in the
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form of Jupyter notebooks to quickly get started.
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Beyond standard _supervised_ learning from data, we'll look at _physical loss_ constraints,
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more tightly coupled learning algorithms with _differentiable simulations_,
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training algorithms tailored to physics problems,
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as well as
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reinforcement learning and uncertainty modeling.
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We live in exciting times: these methods have a huge potential to fundamentally
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change what computer simulations can achieve.
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```{note}
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_What's new in v0.2?_
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For readers familiar with v0.1 of this text, the extended section {doc}`diffphys-examples` and the
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brand new chapter on improved learning methods for physics problems (starting with {doc}`physgrad`) are highly recommended starting points.
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```
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---
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## Coming up
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As a _sneak preview_, the next chapters will show:
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- How to train networks to infer a fluid flow around shapes like airfoils, and estimate the uncertainty of the prediction. This gives a _surrogate model_ that replaces a traditional numerical simulation.
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- How to use model equations as residuals to train networks that represent solutions, and how to improve upon these residual constraints by using _differentiable simulations_.
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- How to more tightly interact with a full simulator for _inverse problems_. 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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- We'll also discuss the importance of _inversion_ for the update steps, and how higher-order information can be used to speed up convergence, and obtain more accurate neural networks.
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Throughout this text,
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we will introduce different approaches for introducing physical models
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into deep learning, i.e., _physics-based deep learning_ (PBDL) approaches.
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These algorithmic variants will be introduced in order of increasing
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tightness of the integration, and the pros and cons of the different approaches
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will be discussed. It's important to know in which scenarios each of the
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different techniques is particularly useful.
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```{admonition} Executable code, 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_, from your browser. You can modify things and
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immediately see what happens -- give it a try by
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[[running this teaser example in your browser]](https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/intro-teaser.ipynb).
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Plus, Jupyter notebooks are great because they're a form of [literate programming](https://en.wikipedia.org/wiki/Literate_programming).
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```
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## Comments and suggestions
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This _book_, where "book" stands for a collection of digital texts and code examples,
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is maintained by the
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[Physics-based Simulation Group](https://ge.in.tum.de) at [TUM](https://www.tum.de).
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Feel free to contact us if you have any comments, e.g., via [old fashioned email](mailto:i15ge@cs.tum.edu).
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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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Btw., we also maintain a [link collection](https://github.com/thunil/Physics-Based-Deep-Learning) with recent research papers.
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```{figure} resources/divider-mult.jpg
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---
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height: 220px
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name: divider-mult
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---
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Some visual examples of numerically simulated time sequences. In this book, we explain how to realize algorithms that use neural networks alongside numerical solvers.
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```
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## Thanks!
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This project would not have been possible without the help of many people who contributed. Thanks to everyone 🙏 Here's an alphabetical list:
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- [Philipp Holl](https://ge.in.tum.de/about/philipp-holl/)
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- [Maximilian Mueller](https://ge.in.tum.de/)
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- [Patrick Schnell](https://ge.in.tum.de/about/patrick-schnell/)
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- [Felix Trost](https://ge.in.tum.de/)
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- [Nils Thuerey](https://ge.in.tum.de/about/n-thuerey/)
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- [Kiwon Um](https://ge.in.tum.de/about/kiwon/)
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Additional thanks go to
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Georg Kohl for the nice divider images (cf. {cite}`kohl2020lsim`),
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Li-Wei Chen for the airfoil data image,
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and to
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Chloe Paillard for proofreading parts of the document.
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% future:
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% - [Georg Kohl](https://ge.in.tum.de/about/georg-kohl/)
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## Citation
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If you find this book useful, please cite it via:
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```
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@book{thuerey2021pbdl,
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title={Physics-based Deep Learning},
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author={Nils Thuerey and Philipp Holl and Maximilian Mueller and Patrick Schnell and Felix Trost and Kiwon Um},
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url={https://physicsbaseddeeplearning.org},
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year={2021},
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publisher={WWW}
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}
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```
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