Predicting synchronization and oscillation death with parallel reservoir computing
Swati Chauhan1, Umesh Kumar Verma1, Swarnendu Mandal2
1Central University of Rajasthan, Department of Physics, Rajasthan, Ajmer-305 817, India.
Physical Review. E
|December 23, 2025
Summary
Parallel parameter-aware reservoir computing accurately predicts critical transitions in multilayer networks. This method forecasts phenomenon transfer and oscillation death, offering insights into complex system dynamics.
Area of Science:
- Complex Systems
- Nonlinear Dynamics
- Computational Neuroscience
Background:
- Reservoir computing is a powerful framework for predicting critical transitions in dynamical systems.
- Multilayer networks exhibit complex dynamics influenced by distinct coupling mechanisms.
- Understanding emergent phenomena and transitions in these networks is crucial.
Purpose of the Study:
- To employ parallel parameter-aware reservoir computing for predicting dynamics in a two-layer multiplex network.
- To investigate the effects of attractive and repulsive coupling on synchronization and oscillation death.
- To accurately predict critical parameter values for phenomenon transfer between layers.
Main Methods:
- Utilized a parallel parameter-aware reservoir computing scheme with two reservoirs, one for each layer.
- Modeled a two-layer multiplex network with attractive coupling in the first layer and repulsive coupling in the second.
- Analyzed the transfer of collective emergent phenomena and induced oscillation death.
Main Results:
- Accurately predicted critical parameter values for the transfer of dynamical phenomena between network layers.
- Observed that interlayer coupling can lead to simultaneous oscillation death in both layers.
- Demonstrated the capability of reservoir computing to forecast transitions in multilayer systems.
Conclusions:
- Parallel parameter-aware reservoir computing is effective for predicting critical transitions in multilayer networks.
- Interlayer coupling plays a significant role in emergent phenomena, including synchronization and oscillation death.
- This approach offers valuable insights for forecasting transitions in complex dynamical systems.
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