Abstract
This paper develops a machine learning scheme to obtain long term predictions on chaotic systems, including high-dimensional, spatiotemporal chaotic systems, by using extremely rare updates
Highlights
A recently emerged interdisciplinary field is a machinelearning-based, model-free prediction of the state evolution of nonlinear/chaotic dynamical systems [1,2,3,4,5,6,7,8,9,10,11,12]
Is it possible to extend significantly the prediction horizon of reservoir computing? We provide an affirmative answer in this Rapid Communication
We develop a physical understanding based on the theory of temporal synchronization
Summary
A recently emerged interdisciplinary field is a machinelearning-based, model-free prediction of the state evolution of nonlinear/chaotic dynamical systems [1,2,3,4,5,6,7,8,9,10,11,12]. With rare data updates, the reservoir computing system can replicate the evolution of the original system within some desired accuracy for an arbitrarily long time, in spite of chaos.
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