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.
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.