I don't think so? Wikipedia suggests that "Physics-informed neural networks" is > a type of universal function approximators that can embed the knowledge of an

I 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...