Learning to predict synchronization of coupled oscillators on randomly generated graphs.

ABSTRACT: Suppose we are given a system of coupled oscillators on an unknown graph along with the trajectory of the system during some period. Can we predict whether the system will eventually synchronize? Even with a known underlying graph structure, this is an important yet analytically intractable question in general. In this work, we take an alternative approach to the synchronization prediction problem by viewing it as a classification problem based on the fact that any given system will eventually synchronize or converge to a non-synchronizing limit cycle. By only using some basic statistics of the underlying graphs such as edge density and diameter, our method can achieve perfect accuracy when there is a significant difference in the topology of the underlying graphs between the synchronizing

SUBMITTER: Bassi H 

PROVIDER: S-EPMC9445105 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature