Large deviations principle for the invariant measures of the 2D stochastic Navier-Stokes equations on a torus

TL;DR

This paper establishes a large deviation principle for the invariant measures of the 2D stochastic Navier-Stokes equations on a torus, providing insights into the probability of rare events in this fluid dynamics model.

Contribution

It proves the large deviation principle for invariant measures of 2D stochastic Navier-Stokes equations with smooth additive noise, a novel result in this context.

Findings

Valid large deviation principle for invariant measures

Insights into rare event probabilities in 2D stochastic fluid dynamics

Extension of large deviations theory to Navier-Stokes on a torus

Abstract

We prove here the validity of a large deviation principle for the family of invariant measures associated to a two dimensional Navier-Stokes equation on a torus, perturbed by a smooth additive noise.