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arXiv · 1805.03362

Attractor Reconstruction by Machine Learning

Zhixin Lu·Brian R. Hunt·Edward Ott
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Abstract

A machine-learning approach called "reservoir computing" has been used successfully for short-term prediction and attractor reconstruction of chaotic dynamical systems from time series data. We present a theoretical framework that describes conditions under which reservoir computing can create an empirical model capable of skillful short-term forecasts and accurate long-term ergodic behavior. We illustrate this theory through numerical experiments. We also argue that the theory applies to certain other machine learning methods for time series prediction.

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Zhixin Lu, Brian R. Hunt, Edward Ott. 2018-06-18. Attractor Reconstruction by Machine Learning. https://doi.org/10.1063/1.5039508

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