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arXiv · 2007.08634

Effective models and predictability of chaotic multiscale systems via machine learning

Francesco Borra·Angelo Vulpiani·Massimo Cencini
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Abstract

We scrutinize the use of machine learning, based on reservoir computing, to build data-driven effective models of multiscale chaotic systems. We show that, for a wide scale separation, machine learning generates effective models akin to those obtained using multiscale asymptotic techniques and, remarkably, remains effective in predictability also when the scale separation is reduced. We also show that predictability can be improved by hybridizing the reservoir with an imperfect model.

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Francesco Borra, Angelo Vulpiani, Massimo Cencini. 2020-07-02. Effective models and predictability of chaotic multiscale systems via machine learning. https://doi.org/10.1103/physreve.102.052203

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