Convergence and ergodicity of splitting methods for fluid models

Data Seminario: 
26-Oct-2021
Ora Inizio: 
11:30
Ora Fine: 
12:30
Andrea Agazzi
Duke University

Abstract: We consider a family of processes obtained by randomly splitting the deterministic flows associated to some fluid models (e.g. Lorenz 96, Galerkin-Naver-Stokes). These split dynamics allow to separate the conservative and dissipative part of the underlying model in a convenient way. We characterize some ergodic properties of these stochastic dynamical systems and prove their convergence to the original deterministic system in the small noise regime, both in the conservative and in the dissipative setting. We then discuss some applications of these models.

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