| Literature DB >> 27872501 |
Dane Taylor1, Per Sebastian Skardal2, Jie Sun3.
Abstract
Synchronization is central to many complex systems in engineering physics (e.g., the power-grid, Josephson junction circuits, and electro-chemical oscillators) and biology (e.g., neuronal, circadian, and cardiac rhythms). Despite these widespread applications-for which proper functionality depends sensitively on the extent of synchronization-there remains a lack of understanding for how systems can best evolve and adapt to enhance or inhibit synchronization. We study how network modifications affect the synchronization properties of network-coupled dynamical systems that have heterogeneous node dynamics (e.g., phase oscillators with non-identical frequencies), which is often the case for real-world systems. Our approach relies on a synchrony alignment function (SAF) that quantifies the interplay between heterogeneity of the network and of the oscillators and provides an objective measure for a system's ability to synchronize. We conduct a spectral perturbation analysis of the SAF for structural network modifications including the addition and removal of edges, which subsequently ranks the edges according to their importance to synchronization. Based on this analysis, we develop gradient-descent algorithms to efficiently solve optimization problems that aim to maximize phase synchronization via network modifications. We support these and other results with numerical experiments.Entities:
Keywords: Kuramoto model; complex networks; network-coupled oscillators; optimization; synchronization; synchrony alignment function
Year: 2016 PMID: 27872501 PMCID: PMC5115605 DOI: 10.1137/16M1075181
Source DB: PubMed Journal: SIAM J Appl Math ISSN: 0036-1399 Impact factor: 2.080