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intro.md
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@@ -24,13 +24,13 @@ Some visual examples of hybrid solvers, i.e. numerical simulators that are enhan
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.
- 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 this behavior by using differentiable simulations.
- 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_.
- Even more tightly coupling a full _rough_ simulator for control problems is another topic. 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 _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.
This _book_, where book stands for a collection of text, equations, images and code examples,
This _book_, where "book" stands for a collection of texts, equations, images and code examples,
is maintained by the
[TUM Physics-based Simulation Group](https://ge.in.tum.de). Feel free to contact us via
[old fashioned email](mailto:i15ge@cs.tum.edu) if you have any comments.
@@ -41,7 +41,7 @@ This collection of materials is a living document, and will grow and change over
Feel free to contribute 😀
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
```{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