Sparse Nonlinear Dynamics Models with SINDy, Part 5: The Optimization Algorithms

This video discusses the various machine learning optimization schemes that may be used for the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm. We discuss the LASSO sparse regression, sequential thresholded least squares (STLS), and the sparse relaxed regularized regression (SR3) algorithms. We also discuss how to enforce partially known physics, such as energy conservation in incompressible fluid flows, by constraining the sparse least squares regression.

Citable link for this video at: https://doi.org/10.52843/cassyni.f1bn05
Original SINDy paper: https://www.pnas.org/content/113/15/3932

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Video to enforce stability: https://www.youtube.com/watch?v=ysg_8yHiO5k
Paper to enforce stability: https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.6.094401

This video was produced at the University of Washington

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0:00 Introduction & Recap
1:56 Parsimonious Modeling with SINDy
9:25 SR3: Sparse Relaxed Regularized Regression
11:27 Constrained SINDy: Enforcing Energy Conservation
16:38 SINDy for Abrupt System Changes