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intro.md
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Welcome ...
============================
Welcome to the Physics-based Deep Learning Book 👋
Welcome to the _Physics-based Deep Learning Book_ 👋
**TL;DR**:
This document targets a variety of combinations of physical simulations with deep learning.
@@ -10,25 +10,25 @@ Beyond standard _supervised_ learning from data, we'll look at _physical loss_ c
more tightly coupled learning algorithms with _differentiable simulations_.
```{figure} resources/teaser.png
```{figure} resources/teaser.jpg
---
height: 220px
name: pbdl-teaser
---
Some visual examples of hybrid solvers, i.e. numerical simulators that are enhanced by trained neural networks.
Some visual examples of numerically simulated time sequences. In this book, we aim for algorithms that use neural networks alongside numerical solvers.
```
% Teaser, simple version:
% ![Teaser, simple version](resources/teaser.png)
% ![Teaser, simple version](resources/teaser.jpg)
## Coming up
As a _sneak preview_, in the next chapters we'll show:
As a _sneak preview_, in the next chapters will show:
- 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.
- We'll show how to use model equations as residual to train networks that represent solutions, and how to improve upon these residual constraints by using _differentiable simulations_.
- 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 _control problems_. E.g., we'll demonstrate how to circumvent the convergence problems of standard reinforcement learning techniques by leveraging simulators in the training loop.
- 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.
This _book_, where "book" stands for a collection of texts, equations, images and code examples,
is maintained by the
@@ -44,7 +44,7 @@ We also maintain a [link collection](https://github.com/thunil/Physics-Based-Dee
```{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_, out of a browser. You can modify things and
can be executed _on the spot_, with 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).
@@ -58,7 +58,6 @@ Oh, and it's great because it's [literate programming](https://en.wikipedia.org/
The contents of the following files would not have been possible without the help of many people. Here's an alphabetical list. Big kudos to everyone 🙏
- [Li-wei Chen](https://ge.in.tum.de/about/dr-liwei-chen/)
- [Philipp Holl](https://ge.in.tum.de/about/)
- [Maximilian Mueller](https://www.tum.de)
- [Patrick Schnell](https://ge.in.tum.de/about/patrick-schnell/)
@@ -82,7 +81,6 @@ See also... Test link: {doc}`supervised`
## TODOs , include
- DP control, show targets at bottom?
- update teaser image
- DP intro, check transpose of Jacobians in equations
- fix phiflow2 , diffphys-code-ns.ipynb