AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning]

This video discusses the third stage of the machine learning process: (3) choosing an architecture with which to represent the model. This is one of the most exciting stages, including all of the new architectures, such as UNets, ResNets, SINDy, PINNs, Operator networks, and many more. There are opportunities to incorporate physics into this stage of the process, such as incorporating known symmetries through custom equivariant layers.

This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company

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00:00 Intro
01:03 The Architecture Zoo/Architectures Overview
06:29 What is Physics?
12:38 Case Study: Pendulum
17:10 Defining a Function Space
20:51 Case Studies: Physics Informed Architectures
23:36 ResNets
24:26 UNets
25:15 Physics Informed Neural Networks
26:50 Lagrangian Neural Networks
27:24 Deep Operator Networks
27:49 Fourier Neural Operators
28:23 Graph Neural Networks
30:02 Invariance and Equivariance
35:59 Outro