update intro

intro.md

@@ -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!
+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.
-
-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.
 
 ```{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}
+}
+```