Learning unidirectional coupling using echo-state network

Swarnendu Mandal, Manish Dev Shrimali
3/23/2023
cs.LG

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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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Cite this paper

@article{mandal2023learning,
  title  = {Learning unidirectional coupling using echo-state network},
  author = {Swarnendu Mandal and Manish Dev Shrimali},
  year   = {2023},
  eprint = {2303.13562},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.13562v1}
}

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