Machine learning, alignment of covariant Lyapunov vectors, and predictability in Rikitake’s geomagnetic dynamo model

DOI10.1063/5.0009765zbMath1445.37021OpenAlexW3047019469WikidataQ98886569 ScholiaQ98886569MaRDI QIDQ5129858

Eduardo L. Brugnago, Jason A. C. Gallas, Marcus W. Beims

Publication date: 2 November 2020

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

Full work available at URL: https://doi.org/10.1063/5.0009765

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Time series analysis of dynamical systems (37M10) Simulation of dynamical systems (37M05) Nonuniformly hyperbolic systems (Lyapunov exponents, Pesin theory, etc.) (37D25)

Related Items (2)

A quasi-periodic route to chaos in a parametrically driven nonlinear medium ⋮ Stability analysis of chaotic systems from data

Cites Work

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• Chaos, fractional kinetics, and anomalous transport
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• Lyapunov, singular and bred vectors in a multi-scale system: an empirical exploration of vectors related to instabilities
• Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
• Using machine learning to predict extreme events in the Hénon map

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