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

Описание: 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

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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