Nature Communications (Feb 2022)

Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds

  • Mattia Cenedese,
  • Joar Axås,
  • Bastian Bäuerlein,
  • Kerstin Avila,
  • George Haller

DOI
https://doi.org/10.1038/s41467-022-28518-y
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 13

Abstract

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Current data-driven modelling techniques perform reliably on linear systems or on those that can be linearized. Cenedese et al. develop a data-based reduced modeling method for non-linear, high-dimensional physical systems. Their models reconstruct and predict the dynamics of the full physical system.