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

Speaker: Steven L. Brunton
00:39/24:05
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Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!
Prof. Steven L. Brunton
Steven L. Brunton
University of Washington

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.

SLB acknowledges support from the National Science Foundation AI Institute in Dynamic Systems (grant number 2112085).

References
  • 1.
    S. L. Brunton et al. (2016) Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences
  • 2.
    S. H. Rudy et al. (2017) Data-driven discovery of partial differential equations. Science Advances
Grants
    National Science Foundation2112085
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Physics Informed Machine Learning
Brunton Lab (University of Washington)
Cite as
S. L. Brunton (2021, August 27), Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!
DOI
doi.org/10.52843/cassyni.sx3npx
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Listed seminar This seminar is open to all
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Video length 24:05