https://doi.org/10.1140/epjp/s13360-025-06069-7
Regular Article
Reconstructing the dynamics of coupled oscillators with cluster synchronization using parameter-aware reservoir computing
School of Mathematics and Statistics, Changchun University of Science and Technology, 130000, Changchun, China
Received:
1
November
2024
Accepted:
29
January
2025
Published online:
8
February
2025
Dynamics reconstruction of complex networks usually requires a large amount of resources; therefore, it is of great significance to find a fast and effective way to achieve this goal. In the study of synchronization dynamics in coupled oscillator networks, complex network structures may be simplified into a smaller-scale network called quotient networks through the external equitable partition. Reservoir computing has demonstrated the capability of rapidly reconstructing system dynamics. In this paper, we attempt to utilize the quotient system in parameter-aware reservoir computing to replace the original network system for training the computer’s neurons, in order to reconstruct the synchronization dynamics of the original network. The system reconstructed by the reservoir computing trained with the quotient network exhibits the same synchronization dynamics, bifurcation diagrams, and spatiotemporal structures as the original system, while the training time is also reduced. The results demonstrate the feasibility of using quotient networks to replace original large-scale networks when reconstructing synchronization dynamics with reservoir computing.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
