Mixing for dynamical systems driven by stationary noises

TL;DR

This paper establishes mixing properties for solutions of certain ODEs and PDEs perturbed by stationary, non-δ-correlated noises, using an iterative method and Kantorovich functionals, applicable to various complex systems.

Contribution

It introduces a general abstract framework for proving mixing in non-Markovian systems driven by stationary noises, extending previous results to broader classes of equations.

Findings

Proved mixing for a wide class of evolution equations.

Applied results to physical models like Navier-Stokes and Ginzburg-Landau.

Extended theory to random processes on 1D lattices.

Abstract

The paper deals with the problem of long-time asymptotic behaviour of solutions for classes of ODEs and PDEs, perturbed by stationary noises. The latter are not assumed to be $\delta$-correlated in time, so that the evolution in question is not necessarily Markovian. We first prove an abstract result which imply the mixing for random dynamical systems satisfying appropriate dissipativity and controllability conditions. It is applicable to a large class of evolution equations, and we illustrate it on the examples of a chain of anharmonic oscillators coupled to heat reservoirs, the 2d Navier-Stokes system, and a complex Ginzburg-Landau equation. Our results also apply to the general theory of random processes on the 1d lattice and allow one to get for them results related to Dobrushin's theorems on reconstructing processes via their conditional distributions. The proof is based on an iterative construction with quadratic convergence. It uses the method of Kantorovich functional, introduced in [KPS02, Kuk02, Kuk06] in the context of randomly forced PDEs, and some ideas suggested in [Shi15, KNS20] to prove mixing with the help of controllability properties of an associated system