Graphop Mean-Field Limits for Kuramoto-Type Models
Marios-Antonios Gkogkas, Christian Kuehn

TL;DR
This paper extends mean-field approximation techniques to Kuramoto models on a broad class of graphs using graphops, providing rigorous results on existence, uniqueness, and convergence of solutions in the mean-field limit.
Contribution
It introduces the use of graphops in VFPEs for the Kuramoto model, broadening the scope of graph limits beyond graphons and establishing rigorous mean-field approximations.
Findings
Proved mean-field approximation for Kuramoto models on general graph limits.
Established existence and uniqueness of solutions involving graphops.
Developed a new metric for graphop convergence and employed Fourier analysis techniques.
Abstract
Originally arising in the context of interacting particle systems in statistical physics, dynamical systems and differential equations on networks/graphs have permeated into a broad number of mathematical areas as well as into many applications. One central problem in the field is to find suitable approximations of the dynamics as the number of nodes/vertices tends to infinity, i.e., in the large graph limit. A cornerstone in this context are Vlasov-Fokker-Planck equations (VFPEs) describing a particle density on a mean-field level. For all-to-all coupled systems, it is quite classical to prove the rigorous approximation by VFPEs for many classes of particle systems. For dense graphs converging to graphon limits, one also knows that mean-field approximation holds for certain classes of models, e.g., for the Kuramoto model on graphs. Yet, the space of intermediate density and sparse…
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