Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants
L. Mars Gao,
J. Nathan Kutz
Abstract:Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy) under
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constraints allows joint discoveries of governing equations and latent coordinate systems from spatio-temporal data, including simulated video frames. However, it is challenging for
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