69 lines
2.9 KiB
Markdown
69 lines
2.9 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**:
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This document targets a variety 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 _physical loss_ constraints, and
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more tightly coupled learning algorithms with _differentiable simulations_.
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```{figure} resources/teaser.jpg
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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 visual examples of numerically simulated time sequences. In this book, we aim for algorithms that use neural networks alongside numerical solvers.
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```
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% Teaser, simple version:
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% 
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## Coming up
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As a _sneak preview_, in the next chapters will show:
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- How to train networks to infer fluid flows around shapes like airfoils in one go, i.e., 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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This _book_, where "book" stands for a collection of texts, 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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We also maintain a [link collection](https://github.com/thunil/Physics-Based-Deep-Learning) with recent research papers.
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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_, with your 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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---
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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/)
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- [Georg Kohl](https://ge.in.tum.de/about/georg-kohl/)
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- [Maximilian Mueller](https://ge.in.tum.de/about/)
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- [Patrick Schnell](https://ge.in.tum.de/about/patrick-schnell/)
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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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