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.

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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.