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.
@article{arxiv.2007.08634,
title = {Effective models and predictability of chaotic multiscale systems via machine learning},
author = {Francesco Borra and Angelo Vulpiani and Massimo Cencini},
journal= {arXiv preprint arXiv:2007.08634},
year = {2020}
}
Comments
12 pages with 10 figures. Accepted in Physical Review E