Data-driven stochastic modeling of coarse-grained dynamics with finite-size effects using Langevin regression

نویسندگان

چکیده

Obtaining coarse-grained models that accurately incorporate finite-size effects is an important open challenge in the study of complex, multi-scale systems. We apply Langevin regression, a recently developed method for finding stochastic differential equation (SDE) descriptions realistically-sampled time series data, to understand Kuramoto model coupled oscillators. find across entire bifurcation diagram, dynamics order parameter are statistically consistent with SDE whose drift term has form predicted by Ott-Antonsen ansatz $N\to \infty$ limit. diffusion nearly independent parameter, and magnitude decaying as $N^{-1/2}$, central limit theorem. This shows diverging fluctuations near critical point driven underlying term, rather than increased forcing.

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ژورنال

عنوان ژورنال: Physica D: Nonlinear Phenomena

سال: 2021

ISSN: ['1872-8022', '0167-2789']

DOI: https://doi.org/10.1016/j.physd.2021.133004