Analysis of chaotic dynamical systems with autoencoders

N. Almazova, G. D. Barmparis, G. P. Tsironis

Research output: Contribution to journalArticlepeer-review

Abstract

We focus on chaotic dynamical systems and analyze their time series with the use of autoencoders, i.e., configurations of neural networks that map identical output to input. This analysis results in the determination of the latent space dimension of each system and thus determines the minimal number of nodes necessary to capture the essential information contained in the chaotic time series. The constructed chaotic autoencoders generate similar maximal Lyapunov exponents as the original chaotic systems and thus encompass their essential dynamical information.

Original languageEnglish
Article number103109
JournalChaos
Volume31
Issue number10
DOIs
Publication statusPublished - Oct 1 2021
Externally publishedYes

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Physics and Astronomy(all)
  • Applied Mathematics

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