Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning - presented by Prof. Steven L. Brunton

Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning

Prof. Steven L. Brunton

Prof. Steven L. Brunton
Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning
Prof. Steven L. Brunton
Steven L. Brunton
University of Washington

In this video, we discuss how deep learning is being used to discover effective coordinate systems where simple dynamical systems models may be discovered.

SLB acknowledges support from the National Science Foundation AI Institute in Dynamic Systems.

References
  • 1.
    K. Champion et al. (2019) Data-driven discovery of coordinates and governing equations. Proceedings of the National Academy of Sciences
  • 2.
    B. Lusch et al. (2018) Deep learning for universal linear embeddings of nonlinear dynamics. Nature Communications
  • 3.
    https://doi.org/10.48550/arXiv.2102.12086
Grants
    National Science Foundation2112085
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Physics Informed Machine Learning
Brunton Lab (University of Washington)
Cite as
S. L. Brunton (2021, August 13), Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning
DOI
doi.org/10.52843/cassyni.4zpjhl
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Listed seminar This seminar is open to all
Recorded Available to all
Video length 26:55