The concentration of zero-noise limits of invariant measures for stochastic dynamical systems

TL;DR

This paper investigates how invariant measures of stochastic differential equations concentrate around stable sets as noise diminishes, using large deviations and Lyapunov functions, and establishes a large deviations principle.

Contribution

It provides new estimates of invariant measures near stable and unstable sets and proves a large deviations principle for these measures in stochastic dynamical systems.

Findings

Invariant measures concentrate on stable sets minimizing a cost functional.

Invariant measures tend to the intersection of stable sets and the Birkhoff center.

Large deviations principle for invariant measures is established.

Abstract

In this paper, we study concentration phenomena of zero-noise limits of invariant measures for stochastic differential equations defined on $\mathbb{R}^d$ with locally Lipschitz continuous coefficients and more than one ergodic state. Under some dissipative conditions, by using Lyapunov-like functions and large deviations methods, we estimate the invariant measures in neighborhoods of stable sets, neighborhoods of unstable sets and their complement, respectively. Our result illustrates that invariant measures concentrate on the intersection of stable sets where a cost functional $W(K_i)$ is minimized and the Birkhoff center of the corresponding deterministic systems as noise tends down to zero. Furthermore, we prove the large deviations principle of invariant measures. At the end of this paper, we provide some explicit examples and their numerical simulations.