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.

%%% 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