Total Variation Error Bounds for the Accelerated Exponential Euler Scheme Approximation of Parabolic Semilinear SPDEs

Abstract

SIAM Journal on Numerical Analysis, Volume 62, Issue 3, Page 1171-1190, June 2024.
Abstract. We prove a new numerical approximation result for the solutions of semilinear parabolic stochastic partial differential equations, driven by additive space-time white noise in dimension 1. The temporal discretization is performed using an accelerated exponential Euler scheme, and we show that, under appropriate regularity conditions on the nonlinearity, the total variation distance between the distributions of the numerical approximation and of the exact solution at a given time converges to 0 when the time-step size vanishes, with order of convergence [math]. Equivalently, weak error estimates with order [math] are thus obtained for bounded measurable test functions. This is an original and major improvement compared with the performance of the standard linear implicit Euler scheme or exponential Euler methods, which do not converge in the sense of total variation when the time-step size vanishes. Equivalently weak error estimates for the standard schemes require twice differentiable test functions. The proof of the total variation error bounds for the accelerated exponential Euler scheme exploits some regularization property of the associated infinite-dimensional Kolmogorov equations.