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Inferring the dynamics of oscillatory systems using recurrent neural networks

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Publication:5227599
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DOI10.1063/1.5096918zbMath1440.37045arXiv1904.03026OpenAlexW3102719883WikidataQ91559954 ScholiaQ91559954MaRDI QIDQ5227599

Markus Abel, Rok Cestnik

Publication date: 6 August 2019

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

Full work available at URL: https://arxiv.org/abs/1904.03026


Mathematics Subject Classification ID

Nonlinear oscillations and coupled oscillators for ordinary differential equations (34C15) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45)



Uses Software

  • TensorFlow


Cites Work

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  • Determining Lyapunov exponents from a time series
  • A practical method for calculating largest Lyapunov exponents from small data sets
  • An equation for continuous chaos
  • Differentiable manifolds
  • Nonlinear Climate Dynamics
  • A recurrent neural network for modelling dynamical systems
  • Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
  • NONPARAMETRIC MODELING AND SPATIOTEMPORAL DYNAMICAL SYSTEMS
  • Floquet theory for partial differential equations
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