Weak convergence rates of spectral Galerkin approximations for SPDEs with nonlinear diffusion coefficients

TL;DR

This paper establishes sharp weak convergence rates for spectral Galerkin approximations of semilinear stochastic evolution equations with nonlinear diffusion, advancing understanding beyond previous results that lacked such rates.

Contribution

It provides the first essentially sharp weak convergence rates for spectral Galerkin methods applied to nonlinear diffusion SPDEs without using Malliavin calculus.

Findings

Sharp weak convergence rates are proven for spectral Galerkin approximations.

The method extends to other spatial, temporal, and noise approximation schemes.

The approach avoids Malliavin calculus by using modified processes and mild Itô formulas.

Abstract

Strong convergence rates for (temporal, spatial, and noise) numerical approximations of semilinear stochastic evolution equations (SEEs) with smooth and regular nonlinearities are well understood in the scientific literature. Weak convergence rates for numerical approximations of such SEEs have been investigated since about 11 years and are far away from being well understood: roughly speaking, no essentially sharp weak convergence rates are known for parabolic SEEs with nonlinear diffusion coefficient functions; see Remark 2.3 in [A. Debussche, Weak approximation of stochastic partial differential equations: the nonlinear case, Math. Comp. 80 (2011), no. 273, 89-117] for details. In this article we solve the weak convergence problem emerged from Debussche's article in the case of spectral Galerkin approximations and establish essentially sharp weak convergence rates for spatial spectral Galerkin approximations of semilinear SEEs with nonlinear diffusion coefficient functions. Our solution to the weak convergence problem does not use Malliavin calculus. Rather, key ingredients in our solution to the weak convergence problem emerged from Debussche's article are the use of appropriately modified versions of the spatial Galerkin approximation processes and applications of a mild It\^{o} type formula for solutions and numerical approximations of semilinear SEEs. This article solves the weak convergence problem emerged from Debussche's article merely in the case of spatial spectral Galerkin approximations instead of other more complicated numerical approximations. Our method of proof extends, however, to a number of other kind of spatial, temporal, and noise numerical approximations for semilinear SEEs.