Attractor reconstruction by machine learning

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.

# Neural Networks and Reservoir Computing # Computer Science # Chaos control and synchronization # Physics and Astronomy # Model Reduction and Neural Networks

Zhixin Lu, Brian R. Hunt, Edward Ott (2018). "Attractor reconstruction by machine learning". Chaos An Interdisciplinary Journal of Nonlinear Science. https://doi.org/10.1063/1.5039508