Mandal Swarnendu, Shrimali Manish Dev
Central University of Rajasthan, Ajmer, Rajasthan 305817, India.
Phys Rev E. 2023 Jun;107(6-1):064205. doi: 10.1103/PhysRevE.107.064205.
Reservoir Computing has found many potential applications in the field of complex dynamics. In this article, we explore 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.
储层计算在复杂动力学领域已发现许多潜在应用。在本文中，我们探究回声状态网络（ESN）模型的卓越能力，使其仅从系统的少数时间序列数据中学习单向耦合方案。我们表明，一旦用驱动 - 响应系统的一些示例动力学进行训练，该机器就能针对具有相同耦合的任何驱动信号预测响应系统的动力学。在训练中，仅需A - B型驱动 - 响应系统的少数时间序列数据就足以让ESN学习耦合方案。训练后，即使我们将驱动系统A替换为不同的系统C，ESN仅使用新驱动系统C的动力学就能再现响应系统B的动力学。
