AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]

This video discusses the fifth stage of the machine learning process: (5) selecting and implementing an optimization algorithm to train the model. There are opportunities to incorporate physics into this stage of the process, such as using constrained optimization to force a model onto a susbpace or submanifold characterized by a symmetry or other physical constraint. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company %%% CHAPTERS %%% 00:00 Intro 01:45 Case Study: KKT Constrained Least Squares 06:18 Case Study: Physics Informed DMD 14:00 Loss vs Optimization of Subspace Constraints 17:50 Subspace Constraints and Symmetry 19:28 Case Study: Symbolic Regression and Evolutionary Optimization 22:25 Parsimony and Sparse Optimization Algorithms 25:03 Case Study: SINDy and SR3 28:38 Parsimony and Sparsity Hyperparameters 30:55 Outro