Learning latent dynamics for partially observed chaotic systems

DOI10.1063/5.0019309zbMath1456.37099arXiv1907.02452OpenAlexW3093625747WikidataQ101160261 ScholiaQ101160261MaRDI QIDQ5139802

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Publication date: 10 December 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1907.02452

zbMATH Keywords

data-driven identificationKoopman operator theoryTakens' embedding theorem

Mathematics Subject Classification ID

Approximation methods and numerical treatment of dynamical systems (37M99)

Related Items

Stochastic embeddings of dynamical phenomena through variational autoencoders ⋮ A framework for machine learning of model error in dynamical systems ⋮ Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning ⋮ Geometry-preserving Lie group integrators for differential equations on the manifold of symmetric positive definite matrices

Uses Software

• ODEPACK
• torchdiffeq

Cites Work

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• On the identification of a vortex
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• Data Assimilation
• Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems

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