HIGH ORDER INTEGRATOR FOR SAMPLING THE INVARIANT DISTRIBUTION OF A CLASS OF PARABOLIC STOCHASTIC PDES WITH ADDITIVE SPACE-TIME NOISE

We introduce a time-integrator to sample with high order of accuracy the invariant distribution for a class of semilinear stochastic PDEs (SPDEs) driven by an additive space-time noise. Combined with a postprocessor, the new method is a modification with negligible overhead of the standard linearized implicit Euler-Maruyama method. We first provide an analysis of the integrator when applied for SDEs (finite dimension), where we prove that the method has order 2 for the approximation of the invariant distribution, instead of 1. We then perform a stability analysis of the integrator in the semilinear SPDE context, and we prove in a linear case that a higher order of convergence is achieved. Numerical experiments, including the semilinear heat equation driven by space-time white noise, confirm the theoretical findings and illustrate the efficiency of the approach.