Additional Topics
=======================
The next sections will give a shorter introduction to other topics that are highly
interesting in the context of physics-based deep learning. These topics (for now) do
not come with executable notebooks, but we will still point to existing open source
implementations for each of them.
![Divider](resources/divider4.jpg)
More specifically, we will look at:
* Model reduction and time series predictions, i.e., using to DL predict the evolution of a physical system in a latent space.
This typically replaces a numerical solver, and we can make use of special techniques from the DL area that target time series.
* Generative models are likewise an own topic in DL, and here especially generative adversarial networks were shown to be powerful tools. They also represent a highly interesting training approach involving to separate NNs.
* Meshless methods and unstructured meshes are an important topic for classical simulations. Here, we'll look at a specific Lagrangian method that employs learning in the context of dynamic, particle-based representations.
% * Finally, metrics to robustly assess the quality of similarity of measurements and results are a central topic for all numerical methods, no matter whether they employ learning or not. In the last section we will look at how DL can be used to learn specialized and improved metrics. {cite}`kohl2020lsim`