Digital Twins of Networked Dynamical Systems: Machine Learning Prediction of Synchronization Transitions in Coupled Chaotic Oscillators
Abstract
Collective behaviors such as synchronization transitions are emergent properties of coupled oscillator systems, and how to reproduce them from data for large-scale systems remains an open problem. Here we propose a model-free coupled reservoir computing (CRC) architecture for constructing digital twins of coupled oscillator systems. The CRC decomposes the twin into three modules that mimic the nodal dynamics, the coupling function, and the coupling network, respectively, so that the modules can be reassembled for different coupled systems. Importantly, it is found that the trained CRC reproduces not only the collective states, including complete synchronization, phase synchronization, and chimera states, but also the synchronization transitions as the coupling strength varies, and the predicted collective behaviors are practically identical to those obtained by direct simulations of the original system. Moreover, the CRC is trained only on the time series of an isolated oscillator and of a pair of coupled oscillators, and the trained modules can be reused for different complex networks and for non-identical oscillators. Our findings establish the CRC architecture as a promising platform for digital twins of complex systems.
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BibTeX
@misc{fan2026digital,
title = {{Digital Twins of Networked Dynamical Systems: Machine Learning Prediction of Synchronization Transitions in Coupled Chaotic Oscillators}},
author = {Huawei Fan and Yao Du and Meng-Xia Bi and Yafeng Wang and Xingang Wang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30632},
eprint = {2610.30632},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30632},
}
RIS
TY - UNPB
TI - Digital Twins of Networked Dynamical Systems: Machine Learning Prediction of Synchronization Transitions in Coupled Chaotic Oscillators
AU - Huawei Fan
AU - Yao Du
AU - Meng-Xia Bi
AU - Yafeng Wang
AU - Xingang Wang
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.30632
UR - https://arxiv.org/abs/2610.30632
AB - Collective behaviors such as synchronization transitions are emergent properties of coupled oscillator systems, and how to reproduce them from data for large-scale systems remains an open problem. Here we propose a model-free coupled reservoir computing (CRC) architecture for constructing digital twins of coupled oscillator systems. The CRC decomposes the twin into three modules that mimic the nodal dynamics, the coupling function, and the coupling network, respectively, so that the modules can be reassembled for different coupled systems. Importantly, it is found that the trained CRC reproduces not only the collective states, including complete synchronization, phase synchronization, and chimera states, but also the synchronization transitions as the coupling strength varies, and the predicted collective behaviors are practically identical to those obtained by direct simulations of the original system. Moreover, the CRC is trained only on the time series of an isolated oscillator and of a pair of coupled oscillators, and the trained modules can be reused for different complex networks and for non-identical oscillators. Our findings establish the CRC architecture as a promising platform for digital twins of complex systems.
ER -