Model reconstruction from temporal data for coupled oscillator networks

Abstract

In a complex system, the interactions between individual agents often lead to emergent collective behavior like spontaneous synchronization, swarming, and pattern formation. The topology of the network of interactions can have a dramatic influence over those dynamics. In many studies, researchers start with a specific model for both the intrinsic dynamics of each agent and the interaction network, and attempt to learn about the dynamics that can be observed in the model. Here we consider the inverse problem: given the dynamics of a system, can one learn about the underlying network? We investigate arbitrary networks of coupled phase-oscillators whose dynamics are characterized by synchronization. We demonstrate that, given sufficient observational data on the transient evolution of each oscillator, one can use machine learning methods to reconstruct the interaction network and simultaneously identify the parameters of a model for the intrinsic dynamics of the oscillators and their coupling.

27 pages, 7 figures, 16 tables

References in corpus (5)

• Synchronization in complex networks
• Chimera states: Coexistence of coherence and incoherence in networks of coupled oscillators
• Revealing networks from dynamics: an introduction
• Reconstruction of a random phase dynamics network from observations
• Reconstructing dynamical networks via feature ranking

Cited by in corpus (9)

• Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links
• Functional Control of Oscillator Networks
• Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests
• Impact of network characteristics on network reconstruction
• Backpropagation on Dynamical Networks
• Reconstructing Network Structures from Partial Measurements
• Data assimilation for networks of coupled oscillators: Inferring unknown model parameters from partial observations
• Iterative procedure for network inference
• Distinguishing pairwise and higher-order interactions in coupled oscillators from time series