Long-term prediction of chaotic systems with machine learning

Huawei Fan, Junjie Jiang, Chun Zhang, Xingang Wang, Ying Cheng Lai

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Reservoir computing systems, a class of recurrent neural networks, have recently been exploited for model-free, data-based prediction of the state evolution of a variety of chaotic dynamical systems. The prediction horizon demonstrated has been about half dozen Lyapunov time. Is it possible to significantly extend the prediction time beyond what has been achieved so far? We articulate a scheme incorporating time-dependent but sparse data inputs into reservoir computing and demonstrate that such rare "updates"of the actual state practically enable an arbitrarily long prediction horizon for a variety of chaotic systems. A physical understanding based on the theory of temporal synchronization is developed.

Original languageEnglish (US)
Article number012080
JournalPhysical Review Research
Volume2
Issue number1
DOIs
StatePublished - Mar 2020

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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