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In a complex system, the interactions between individual agents often lead toemergent collective behavior like spontaneous synchronization, swarming, andpattern formation. The topology of the network of interactions can have adramatic influence over those dynamics. In many studies, researchers start witha specific model for both the intrinsic dynamics of each agent and theinteraction network, and attempt to learn about the dynamics that can beobserved in the model. Here we consider the inverse problem: given the dynamicsof a system, can one learn about the underlying network? We investigatearbitrary networks of coupled phase-oscillators whose dynamics arecharacterized by synchronization. We demonstrate that, given sufficientobservational data on the transient evolution of each oscillator, one can usemachine learning methods to reconstruct the interaction network andsimultaneously identify the parameters of a model for the intrinsic dynamics ofthe oscillators and their coupling.

Model reconstruction from temporal data for coupled oscillator networks