Reconstruction of a random phase dynamics network from observations
arXiv:1711.06453 · doi:10.1016/j.physleta.2017.11.012
Abstract
We consider networks of coupled phase oscillators of different complexity: Kuramoto-Daido-type networks, generalized Winfree networks, and hypernetworks with triple interactions. For these setups an inverse problem of reconstruction of the network connections and of the coupling function from the observations of the phase dynamics is addressed. We show how a reconstruction based on the minimization of the squared error can be implemented in all these cases. Examples include random networks with full disorder both in the connections and in the coupling functions, as well as networks where the coupling functions are taken from experimental data of electrochemical oscillators. The method can be directly applied to asynchronous dynamics of units, while in the case of synchrony, additional phase resettings are necessary for reconstruction.
References in corpus (4)
- Inference of Time-Evolving Coupled Dynamical Systems in the Presence of Noise
- Dynamical Bayesian Inference of Time-evolving Interactions: From a Pair of Coupled Oscillators to Networks of Oscillators
- Finite-size-induced transitions to synchrony in oscillator ensembles with nonlinear global coupling
- Direct extraction of phase dynamics from fluctuating rhythmic data based on a Bayesian approach