115 lines
5.5 KiB
Markdown
115 lines
5.5 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_ (v0.3, the _GenAI_ edition) 👋
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**TL;DR**:
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This document is a hands-on, comprehensive guide to deep learning in the realm of physical simulations. Rather than just theory, we emphasize practical application: every concept is paired with interactive Jupyter notebooks to get you up and running quickly. Beyond traditional supervised learning, we dive into physical _loss-constraints_, _differentiable_ simulations, _diffusion-based_ approaches for _probabilistic generative AI_, as well as reinforcement learning and advanced neural network architectures. These foundations are paving the way for the next generation of scientific _foundation models_.
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We are living in an era of rapid transformation. These methods have the potential to redefine what’s possible in computational science.
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```{note}
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_What's new in v0.3?_
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This latest edition takes things even further with a major new chapter on generative modeling, covering cutting-edge techniques like denoising, flow-matching, autoregressive learning, physics-integrated constraints, and diffusion-based graph networks. We've also introduced a dedicated section on neural architectures specifically designed for physics simulations. All code examples have been updated to leverage the latest frameworks.
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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 neural networks to [predict the fluid flow around airfoils with diffusion modeling](probmodels-ddpm-fm). This gives a probabilistic _surrogate model_ that replaces and outperforms traditional simulators.
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- How to use model equations as residuals to train networks that [represent solutions](diffphys-dpvspinn), and how to improve upon these residual constraints by using [differentiable simulations](diffphys-code-sol).
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- How to more tightly interact with a full simulator for [inverse problems](diffphys-code-control). E.g., we'll demonstrate how to circumvent the convergence problems of standard reinforcement learning techniques by leveraging [simulators in the training loop](reinflearn-code).
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- We'll also discuss the importance of [choosing the right network architecture](supervised-arch): whether to consider global or local interactions, continuous or discrete representations, and structured versus unstructured graph meshes.
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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 the many people who contributed to it. A big thanks to everyone 🙏 Here's an alphabetical list:
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- [Benjamin Holzschuh](https://ge.in.tum.de/about/)
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- [Philipp Holl](https://ge.in.tum.de/about/philipp-holl/)
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- [Georg Kohl](https://ge.in.tum.de/about/georg-kohl/)
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- [Mario Lino](https://ge.in.tum.de/about/mario-lino/)
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- [Qiang Liu](https://ge.in.tum.de/about/qiang-liu/)
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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/about/)
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- [Nils Thuerey](https://ge.in.tum.de/about/n-thuerey/)
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Additional thanks go to
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Li-Wei Chen,
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Xin Luo,
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Maximilian Mueller,
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Chloe Paillard,
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Kiwon Um,
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and all github contributors!
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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={N. Thuerey and B. Holzschuh and P. Holl and G. Kohl and M. Lino and Q. Liu and P. Schnell and F. Trost},
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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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## Time to get started
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The future of simulation is being rewritten, and with the following AI and deep learning techniques, you’ll be at the forefront of these developments. Let’s dive in!
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