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@ -4,7 +4,8 @@ Welcome ...
Welcome to the _Physics-based Deep Learning Book_ 👋
**TL;DR**:
This document targets a variety of combinations of physical simulations with deep learning.
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
@ -34,19 +35,15 @@ 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!
This collection of materials is a living document, and will grow and change over time.
Feel free to contribute 😀
We also maintain a [link collection](https://github.com/thunil/Physics-Based-Deep-Learning) with recent research papers.
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_, with your browser. You can modify things and
can be executed _on the spot_, from your browser. You can modify things and
immediately see what happens -- give it a try...
<br><br>
Oh, and it's great because it's [literate programming](https://en.wikipedia.org/wiki/Literate_programming).
Plus, jupyter notebooks are great because they're a form of [literate programming](https://en.wikipedia.org/wiki/Literate_programming).
```
@ -58,11 +55,22 @@ Oh, and it's great because it's [literate programming](https://en.wikipedia.org/
This project would not have been possible without the help of many people who contributed. Thanks to everyone 🙏 Here's an alphabetical list:
- [Philipp Holl](https://ge.in.tum.de/about/)
- [Georg Kohl](https://ge.in.tum.de/about/georg-kohl/)
% - [Georg Kohl](https://ge.in.tum.de/about/georg-kohl/)
- [Maximilian Mueller](https://ge.in.tum.de/)
- [Patrick Schnell](https://ge.in.tum.de/about/patrick-schnell/)
- [Felix Trost](https://ge.in.tum.de/)
- [Nils Thuerey](https://ge.in.tum.de/about/n-thuerey/)
- [Kiwon Um](https://ge.in.tum.de/about/kiwon/)
## Citation
If you find this book useful, please cite via:
```
@article{thuerey2021pbdl,
title={Physics-based Deep Learning},
author={Thuerey, Nils and Maximilian Mueller and Patrick Schnell and Felix Trost and Kiwon Um},
url={http://physicsbaseddeeplearning.org},
year={2021},
publisher={www}
}
```