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@@ -19,14 +19,7 @@ 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
@@ -47,15 +40,6 @@ 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
@@ -66,7 +50,24 @@ Plus, Jupyter notebooks are great because they're a form of [literate programmin
```
![Divider](resources/divider3.jpg)
## 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.
```{figure} resources/divider-mult.jpg
---
height: 220px
name: divider-mult
---
Some visual examples of numerically simulated time sequences. In this book, we explain how to realize algorithms that use neural networks alongside numerical solvers.
```
## Thanks!
@@ -89,7 +90,6 @@ Chloe Paillard for proofreading parts of the document.
% future:
% - [Georg Kohl](https://ge.in.tum.de/about/georg-kohl/)
## Citation
If you find this book useful, please cite it via: