Coarse Graining the Dynamics of Heterogeneous Oscillators in Networks with Spectral Gaps
arXiv:1105.4144 · doi:10.1103/PhysRevE.84.036708
Abstract
We present a computer-assisted approach to coarse-graining the evolutionary dynamics of a system of nonidentical oscillators coupled through a (fixed) network structure. The existence of a spectral gap for the coupling network graph Laplacian suggests that the graph dynamics may quickly become low-dimensional. Our first choice of coarse variables consists of the components of the oscillator states -their (complex) phase angles- along the leading eigenvectors of this Laplacian. We then use the equation-free framework [1], circumventing the derivation of explicit coarse-grained equations, to perform computational tasks such as coarse projective integration, coarse fixed point and coarse limit cycle computations. In a second step, we explore an approach to incorporating oscillator heterogeneity in the coarse-graining process. The approach is based on the observation of fastdeveloping correlations between oscillator state and oscillator intrinsic properties, and establishes a connection with tools developed in the context of uncertainty quantification.
11 pages, 9 figures
References in corpus (8)
- Finding community structure in networks using the eigenvectors of matrices
- Synchronization in complex networks
- Synchronization reveals topological scales in complex networks
- Synchronization processes in complex networks
- Laplacian Spectra as a Diagnostic Tool for Network Structure and Dynamics
- Spectral Coarse Graining and Synchronization in Oscillator Networks
- Analysis of Nonlinear Synchronization Dynamics of Oscillator Networks by Laplacian Spectral Methods
- Coarse-graining the dynamics of coupled oscillators
Cited by in corpus (7)
- Dynamical Systems on Networks: A Tutorial
- Threefold way to the dimension reduction of dynamics on networks: an application to synchronization
- Spectral coarse graining for random walk in bipartite networks
- Data-driven Selection of Coarse-Grained Models of Coupled Oscillators
- Equation-free analysis of a dynamically evolving multigraph
- Data-driven stochastic modeling of coarse-grained dynamics with finite-size effects using Langevin regression
- Modeling Heterogeneity in Networks using Uncertainty Quantification Tools