updates intro & motivation

intro.md

@@ -9,6 +9,16 @@ As much as possible, the algorithms will come with hands-on code examples to qui
 Beyond standard _supervised_ learning from data, we'll look at loss constraints, and 
 more tightly coupled learning algorithms with differentiable simulations.
 
+
+```{figure} ./resources/teaser.png
+---
+height: 220px
+name: pbdl-teaser
+---
+Some examples ... preview teaser ...
+```
+
+
 As a _sneak preview_, in the next chapters we'll show:
 
 - How to train networks to infer fluid flow solutions around shapes like airfoils in one go, i.e., without needing a simulator.
@@ -24,7 +34,35 @@ is maintained by the
 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!
 
-TODO, add teaser pic
+This collection of materials is a living document, and will grow and change over time. 
+Feel free to contribute 😀
+
+[TUM Physics-based Simulation Group](https://ge.in.tum.de).
+
+We also maintain a [link collection](https://github.com/thunil/Physics-Based-Deep-Learning) with recent research papers.
+
+```{admonition} Code, executable, 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 
+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).
+```
+
+## Specifically
+
+To be a bit more specific, _physics_ is a huge field, we can't cover everything... 
+
+```{note}
+For now our focus is:
+- field-based simulations , less Lagrangian
+- simulations, not experiments
+- combination with _deep learning_ (plenty of other interesting ML techniques)
+```
+
+---
+
 
 ## Thanks!
 
@@ -34,17 +72,26 @@ The contents of the following files would not have been possible without the hel
 - Ms. y
 - ...
 
+% tests...
+
+
+a b c
+
+```{admonition} My title2
+:class: seealso
+See also... Test link: {doc}`supervised`
+```
 
 
 % ----------------
+---
 
 
-===
+## Planned content
 
-## Planned content, loose collection of notes and TODOs:
+Loose collection of notes and TODOs:
 
 General physics & dl , intro & textual overview
-    more general intro: https://github.com/thunil/Physics-Based-Deep-Learning
 
 Supervised? Airfoils? Liwei, simple example? app: optimization, shape opt w surrogates
 
@@ -86,7 +133,23 @@ PGa 2020 Sept, content: ML & opt
 PGb 201002-beforeVac, content: v1,v2,old - more PG focused
     -> general intro versions
 
-[MISSING, time series, sequence prediction?] {cite}`wiewel2019lss,bkim2019deep,wiewel2020lsssubdiv`
+TODO, for version 2.x add: 
+
+time series, sequence prediction?] {cite}`wiewel2019lss,bkim2019deep,wiewel2020lsssubdiv`
+    include DeepFluids variant?
+
 [BAYES , prob?]
+    include results Jakob
+
 [unstruct / lagrangian] {cite}`prantl2019tranquil,ummenhofer2019contconv`
-Outlook
+    include ContConv / Lukas
+
+
+---
+
+_Misc jupyter book TODOs_
+
+- Fix latex PDF output
+- How to include links in references?
+
+