Abstract
The Kuramoto model has been introduced to describe synchronization phenomena observed in groups of cells, individuals, circuits, etc. The model consists of $N$ interacting oscillators on the one dimensional sphere $\mathbf{S}^{1}$, driven by independent Brownian Motions with constant drift chosen at random. This quenched disorder is chosen independently for each oscillator according to the same law $\mu$. The behaviour of the system for large $N$ can be understood via its empirical measure: we prove here the convergence as $N\to\infty$ of the quenched empirical measure to the unique solution of coupled McKean-Vlasov equations, under weak assumptions on the disorder $\mu$ and general hypotheses on the interaction. The main purpose of this work is to address the issue of quenched fluctuations around this limit, motivated by the dynamical properties of the disordered system for large but fixed $N$. Whereas we observe a self-averaging for the law of large numbers, this no longer holds for the corresponding central limit theorem: the trajectories of the fluctuations process are sample-dependent.
Highlights
We study the fluctuations in the Kuramoto model, which is a particular case of interacting diffusions with a mean field Hamiltonian that depends on a random disorder
The Kuramoto model is a particular case of a system of N oscillators solutions to the following SDE: (1)
The purpose of this paper is to address the issue of both convergence and fluctuations of the empirical measure, as N → ∞ ; the main theorem of this paper (Theorem 2.10)
Summary
We study the fluctuations in the Kuramoto model, which is a particular case of interacting diffusions with a mean field Hamiltonian that depends on a random disorder. It seems quite clear even at a superficial level that if we consider the central limit theorem associated to this convergence, self-averaging does not hold since the fluctuations of the disorder compete with the dynamical fluctuations This leads for example to a remarkable phenomenon (pointed out e.g. in [2] on the basis of numerical simulations): even if the distribution μ is symmetric, the fluctuations of a fixed chosen sample of the disorder makes it not symmetric and√ the center of the synchronization of the system slowly (i.e. with a speed of order 1/ N ) rotates in one direction and with a speed that depends on the sample of the disorder (Fig. 1 and 2).
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