AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]

Video Description

This video discusses the first stage of the machine learning process: (1) formulating a problem to model. There are lots of opportunities to incorporate physics into this process, and learn new physics by applying ML to the right problem. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company %%% CHAPTERS %%% 00:00 Intro 04:51 Deciding on the Problem 07:08 Why do you need an ML Model? 14:54 Case Study: Super Resolution 17:07 Case Study: Discovering New Physics 18:37 Case Study: Materials Discovery 19:12 Case Study: Computational Chemistry 20:50 Case Study: Digital Twins & Discrepancy Models 21:56 Case Study: Shape Optimization 25:13 The Digital Twin 29:16 Modeling the Math 33:31 Modeling the Chaos 34:18 Case Study: Climate Modeling 35:08 Benchmark Systems 35:47 Case Study: Turbulence Closure Modeling 39:16 When not to use Machine Learning 42:15 Outro