mlsanityData to 7 Oct 2026

Learning unidirectional coupling using echo-state network

arXiv 2303.13562 ↗First posted 23 Mar 2023cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 54 references that are in the map. First posted on arXiv 23 Mar 2023.

Builds on

12 of 54 references.

References · 12

  1. 2020Physical reservoir computing -- An introductory perspectivecited by 58
  2. 2019Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computerscited by 43
  3. 2021Adaptable Hamiltonian neural networkscited by 21
  4. 2021Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computingcited by 21
  5. 2020Forecasting Hamiltonian dynamics without canonical coordinatescited by 17
  6. 2016Bayesian optimization of hyper-parameters in reservoir computingcited by 13
  7. 2019Separation of Chaotic Signals by Reservoir Computingcited by 12
  8. 2020Learn to Synchronize, Synchronize to Learncited by 11
  9. 2021Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learningcited by 3
  10. 2015On the low dimensional dynamics of structured random networkscited by 2
  11. 2018Critical Neuromorphic Computing based on Explosive Synchronizationcited by 2
  12. 2021Role of assortativity in predicting burst synchronization using echo state networkcited by 1

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Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2024Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMscited by 9
  2. 2023Suppressing unknown disturbances to dynamical systems using machine learningcited by 1