Large deviations for invariant measure of stochastic Allen-Cahn equation with inhomogeneous boundary conditions and multiplicative noise

Abstract

We establish a small-noise large deviation principle for the family of invariant measures $\{\mu_\epsilon\}_{\epsilon>0}$ associated with the one-dimensional stochastic Allen-Cahn equation, subject to inhomogeneous Dirichlet boundary conditions and driven by unbounded multiplicative noise. The main novelty is that the deterministic system is only weakly dissipative, while the noise coefficient is allowed to have strictly sublinear growth arbitrarily close to linear. Using L. Simon's convergence theorem, we prove that every trajectory of the corresponding noiseless equation converges, as time tends to infinity, to the unique minimiser of the Ginzburg-Landau energy functional determined by the boundary conditions. A key ingredient is an exponential estimate for the invariant measures outside bounded subsets of $W^{k^\star,p^\star}$, where $k^\star p^\star>1$ and $p^\star$ are sufficiently large; such subsets are compact in the underlying space of continuous functions. As a consequence of the large deviation principle, we show that, as $\epsilon\to 0$, the invariant measures $\mu_\epsilon$ concentrate exponentially fast around the unique minimiser.