Work

Attractor reconstruction by machine learning

Year: 2018

Type: article

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.

Source: Chaos An Interdisciplinary Journal of Nonlinear Science

DOI: 10.1063/1.5039508

Authors: Zhixin Lu, Brian R. Hunt, Edward Ott

Institutions: University of Maryland, College Park, University of Pennsylvania

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Defense Advanced Research Projects Agency, Army Research Office

Award: G683518676