4 replies
19h6m
TBC, this is about deep learning for physics problems, not a general approach to deep learning from a physicist's perspective.
This document contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started. Beyond standard supervised learning from data, we’ll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, training algorithms tailored to physics problems, as well as reinforcement learning and uncertainty modeling. We live in exciting times: these methods have a huge potential to fundamentally change what computer simulations can achieve.
That would have been the more interesting book. There is a lot of intuition that statistical mechanics could bring to deep learning.
I would have called this one Deep Learning for Physics.
I guess considering their group is called the Physics-based Simulation Group [1], I'm thinking maybe that's just the terminology they've always used? Or maybe it's a German->English translation thing?
[1] https://ge.in.tum.de/
I think the other one is more commonly known as “physics informed deep learning”.
I don't think so? Wikipedia suggests that "Physics-informed neural networks" is
https://en.wikipedia.org/wiki/Physics-informed_neural_networ...
In other words, that seems to refer to giving the model prior info (a bias) about physical laws that generated the data. What I'm talking about is more abstract: using physics-y type math ideas to understand the internal behavior of the networks. Here are a couple examples:
https://proceedings.neurips.cc/paper_files/paper/2023/hash/6...
https://cgad.ski/blog/where-is-noethers-principle-in-machine...