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arXiv · 2303.13562

Learning unidirectional coupling using echo-state network

Swarnendu Mandal·Manish Dev Shrimali
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Abstract

Reservoir Computing has found many potential applications in the field of complex dynamics. In this article, we exploit the exceptional capability of the echo-state network (ESN) model to make it learn a unidirectional coupling scheme from only a few time series data of the system. We show that, once trained with a few example dynamics of a drive-response system, the machine is able to predict the response system's dynamics for any driver signal with the same coupling. Only a few time series data of an $A-B$ type drive-response system in training is sufficient for the ESN to learn the coupling scheme. After training even if we replace drive system $A$ with a different system $C$, the ESN can reproduce the dynamics of response system $B$ using the dynamics of new drive system $C$ only.

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Swarnendu Mandal, Manish Dev Shrimali. 2023-03-23. Learning unidirectional coupling using echo-state network. https://doi.org/10.1103/physreve.107.064205

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