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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 pract
by jessriedel 2y ago
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.
- deleted 2y ago[deleted]
- esafak 2y agoThat 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.
- beefok 2y agoI 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/ https://ge.in.tum.de/
- ayhanfuat 2y agoI think the other one is more commonly known as “physics informed deep learning”.
- jessriedel 2y agoI don't think so? Wikipedia suggests that "Physics-informed neural networks" is > a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process https://en.wikipedia.org/wiki/Physics-informed_neural_networks 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/6e4432b912599d11609b9cdf98c823c5-Abstract-Conference.html https://proceedings.neurips.cc/paper_files/paper/2023/hash/6... https://cgad.ski/blog/where-is-noethers-principle-in-machine-learning.html https://cgad.ski/blog/where-is-noethers-principle-in-machine...
- d_tr 2y agoYeah, I came here expecting methods of mathematical physics applied to deep learning. I'd suggest a title change. Still a great topic though, no doubt!