REVIEW 2 cited by
Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We develop data-driven methods for incorporating physical information for priors to learn parsimonious representations of nonlinear systems arising from parameterized PDEs and mechanics. Our approach is based on Variational Autoencoders (VAEs) for learning from observations nonlinear state space models. We develop ways to incorporate geometric and topological priors through general manifold latent space representations. We investigate the performance of our methods for learning low dimensional representations for the nonlinear Burgers equation and constrained mechanical systems.
Forward citations
Cited by 2 Pith papers
-
Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature
Curvature-flow-regularized latent spaces improve mean out-of-distribution errors for Burger's equation relative to a plain autoencoder, but the flows are heuristic and the evidence is limited.
-
Data-driven discovery of dynamical models in biology
A review benchmarking regression, network, and decomposition methods on the Oregonator model under the Koopman operator framework, with illustrative experiments on simulated data.
Discussion (0). Continue with ORCID to comment.