Digital Twins of Networked Dynamical Systems: Machine Learning Prediction of Synchronization Transitions in Coupled Chaotic Oscillators
The paper proposes a model-free Coupled Reservoir Computing (CRC) architecture to create digital twins of coupled oscillator systems. This model decomposes the twin into modules mimicking nodal dynamics, coupling functions, and coupling networks. The trained CRC successfully predicts collective states, including synchronization transitions and chimera states, with accuracy matching direct simulations.