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The Kuramoto Model on Power Law Graphs: Synchronization and Contrast States
- Source :
- Journal of Nonlinear Science. 30:2405-2427
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- The relation between the structural properties of the network and its dynamics is a central question in the analysis of dynamical networks. It is especially relevant for complex networks found in real-world applications. This work presents mathematically rigorous analysis of coupled dynamical systems on power law graphs. Specifically, we study large systems of coupled Kuramoto phase oscillators. In the limit as the size of the network tends to infinity, we derive analytically tractable mean field partial differential equation for the probability density function describing the state of the coupled system. The mean field limit is used to establish an explicit formula for the synchronization threshold for coupled phase oscillators with randomly distributed intrinsic frequencies. Furthermore, we study stable spatial patterns generated by the Kuramoto model with repulsive coupling. In particular, we identify a family of stable steady-state solutions having multiple regions with distinct statistical properties. We call these solutions contrast states. Like chimera states, contrast states exhibit coexisting regions of highly localized (coherent) behavior and highly irregular (incoherent) distribution of phases. We provide a detailed mathematical analysis of contrast states in the KM using the Ott–Antonsen ansatz. The analysis of synchronization and contrast states provides new insights into the role of power law connectivity in shaping dynamics of coupled dynamical systems. In particular, we show that despite sparse connectivity, power law networks possess remarkable synchronizability: the synchronization threshold can be made arbitrarily low by varying the parameter of the power law distribution.
- Subjects :
- Physics
Dynamical systems theory
Applied Mathematics
Kuramoto model
General Engineering
Probability density function
Complex network
01 natural sciences
Power law
010305 fluids & plasmas
010101 applied mathematics
symbols.namesake
Modeling and Simulation
0103 physical sciences
Synchronization (computer science)
symbols
Pareto distribution
Statistical physics
0101 mathematics
Ansatz
Subjects
Details
- ISSN :
- 14321467 and 09388974
- Volume :
- 30
- Database :
- OpenAIRE
- Journal :
- Journal of Nonlinear Science
- Accession number :
- edsair.doi...........380b6a83d982e8d5aeb1c92c8f38a4c7
- Full Text :
- https://doi.org/10.1007/s00332-018-9489-3