Long-term prediction of chaotic systems with machine learning

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 signi...

Full description

Saved in:
Bibliographic Details
Published in:Physical review research 2020-03, Vol.2 (1), p.012080, Article 012080
Main Authors: Fan, Huawei, Jiang, Junjie, Zhang, Chun, Wang, Xingang, Lai, Ying-Cheng
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2643-1564
2643-1564
DOI:10.1103/PhysRevResearch.2.012080