Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!

Machine learning is enabling the discovery of dynamical systems models and governing equations purely from measurement data. Five years after the original SINDy paper, we revisit this topic, describing the algorithm and exploring the main challenges for computing sparse nonlinear models from data. This is part of a multi-part series.

Original SINDy paper: https://www.pnas.org/content/113/15/3932
SINDy for PDEs: https://advances.sciencemag.org/content/3/4/e1602614
Citable link for this video at: https://doi.org/10.52843/cassyni.sx3npx

Joint work with Nathan Kutz: https://www.youtube.com/channel/UCoUOaSVYkTV6W4uLvxvgiFA

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This video was produced at the University of Washington