Invariant measure of the stochastic Allen-Cahn equation: the regime of small noise and large system size

TL;DR

This paper analyzes the invariant measure of the 1D stochastic Allen-Cahn equation under small noise and large system size, revealing the probability of transitions and the distribution of transition layers.

Contribution

It provides sharp exponential bounds on transition probabilities and characterizes the distribution of transition layer positions in the regime of small noise and large system size.

Findings

Probability of a single transition is exponentially close to one.

Transition layer position is uniformly distributed over the system.

Bounds are sharp on the exponential scale.

Abstract

We study the invariant measure of the one-dimensional stochastic Allen-Cahn equation for a small noise strength and a large but finite system. We endow the system with inhomogeneous Dirichlet boundary conditions that enforce at least one transition from -1 to 1. (Our methods can be applied to other boundary conditions as well.) We are interested in the competition between the energy that should be minimized due to the small noise strength and the entropy that is induced by the large system size. Our methods handle system sizes that are exponential with respect to the inverse noise strength, up to the critical exponential size predicted by the heuristics. We capture the competition between energy and entropy through upper and lower bounds on the probability of extra transitions between -1 and 1. These bounds are sharp on the exponential scale and imply in particular that the probability of having one and only one transition from -1 to +1 is exponentially close to one. In addition, we show that the position of the transition layer is uniformly distributed over the system on scales larger than the logarithm of the inverse noise strength. Our arguments rely on local large deviation bounds, the strong Markov property, the symmetry of the potential, and measure-preserving reflections.