Model-Free Prediction of Chaotic Systems Using High Efficient Next-generation Reservoir Computing

TL;DR

This paper introduces a new reservoir computing paradigm that enables efficient, model-free prediction of both low-dimensional and large-scale chaotic systems, outperforming existing methods in accuracy and computational efficiency.

Contribution

A novel reservoir computing framework is proposed that improves prediction length, reduces training data needs, and lowers computational costs for chaotic systems.

Findings

Outperforms recent reservoir computing methods in prediction accuracy.

Requires less training data and computational resources.

Successfully predicts Lorenz and Kuramoto-Sivashinsky systems.

Abstract

To predict the future evolution of dynamical systems purely from observations of the past data is of great potential application. In this work, a new formulated paradigm of reservoir computing is proposed for achieving model-free predication for both low-dimensional and very large spatiotemporal chaotic systems. Compared with traditional reservoir computing models, it is more efficient in terms of predication length, training data set required and computational expense. By taking the Lorenz and Kuramoto-Sivashinsky equations as two classical examples of dynamical systems, numerical simulations are conducted, and the results show our model excels at predication tasks than the latest reservoir computing methods.