Name: Reconstructing attractors with autoencoders
Keywords: Dynamical systems and ergodic theory, Physics - Data Analysis, Statistics and Probability, FOS: Physical sciences, Adaptation and Self-Organizing Systems (nlin.AO), Ordinary differential equations, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Data Analysis, Statistics and Probability (physics.data-an)

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2025Embargo end date: 01 Jan 2024 English Publisher:AIP PublishingJournal:Chaos: An Interdisciplinary Journal of Nonlinear Science, volume 35 (issn: 1054-1500, eissn: 1089-7682,

Authors: F. Fainstein; G. B. Mindlin; P. Groisman;

doi: 10.1063/5.0232584 , 10.48550/arxiv.2404.16855

pmid: 39752197

arXiv: 2404.16855

Reconstructing attractors with autoencoders

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Abstract

We propose a method based on autoencoders to reconstruct attractors from recorded footage, preserving the topology of the underlying phase space. We provide theoretical support and test the method with (i) footage of the temperature and stream function fields involved in the Lorenz atmospheric convection problem and (ii) a time series obtained by integrating the Rössler equations.

Related Organizations

University of Buenos Aires
Argentina
National Scientific and Technical Research Council
Argentina

Keywords

Dynamical systems and ergodic theory, Physics - Data Analysis, Statistics and Probability, FOS: Physical sciences, Adaptation and Self-Organizing Systems (nlin.AO), Ordinary differential equations, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Data Analysis, Statistics and Probability (physics.data-an)

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