4.1 KiB
Welcome …
Welcome to the Physics-based Deep Learning Book 👋
TL;DR: This document targets a practical and comprehensive introduction to the latest concepts for combining 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 physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, as well as extensions such as reinforcement learning and uncertainty modeling. We live in exciting times: these methods have a huge potential to fundamentally change what we can achieve with simulations.
```{figure} resources/teaser.jpg |
---|
height: 220px |
name: pbdl-teaser |
Some visual examples of numerically simulated time sequences. In this book, we explain how to realize algorithms that use neural networks alongside numerical solvers.
## Coming up
As a _sneak preview_, in the next chapters will show:
- 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.
- 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_.
- 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.
Over the course of the next
chapters we will introduce different approaches for introducing physical models
into deep learning, i.e., _physics-based deep learning_ (PBDL) approaches.
These algorithmic variants will be introduced in order of increasing
tightness of the integration, and pros and cons of the different approaches
will be discussed. It's important to know in which scenarios each of the
different techniques is particularly useful.
## Comments and suggestions
This _book_, where "book" stands for a collection of digital texts and code examples,
is maintained by the
[TUM Physics-based Simulation Group](https://ge.in.tum.de). Feel free to contact us
if you have any comments, e.g., via [old fashioned email](mailto:i15ge@cs.tum.edu).
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 😀! Btw., we also maintain a [link collection](https://github.com/thunil/Physics-Based-Deep-Learning) with recent research papers.
```{admonition} Executable code, right here, right now
:class: tip
We focus on jupyter notebooks, a key advantage of which is that all code examples
can be executed _on the spot_, from your browser. You can modify things and
immediately see what happens -- give it a try...
<br><br>
Plus, jupyter notebooks are great because they're a form of [literate programming](https://en.wikipedia.org/wiki/Literate_programming).

Thanks!
This project would not have been possible without the help of many people who contributed. Thanks to everyone 🙏 Here’s an alphabetical list:
Additional thanks go to Georg Kohl for the nice divider images
(cf. {cite}kohl2020lsim
), Li-Wei Chen for the airfoil data
image, and to Chloe Paillard for proofreading parts of the document.
% future: % - Georg Kohl
Citation
If you find this book useful, please cite it via:
@book{thuerey2021pbdl,
title={Physics-based Deep Learning},
author={Nils Thuerey and Philipp Holl and Maximilian Mueller and Patrick Schnell and Felix Trost and Kiwon Um},
url={http://physicsbaseddeeplearning.org},
year={2021},
publisher={WWW}
}