Crank-Nicolson finite element approximations for a linear stochastic heat equation with additive space-time white noise
This work provides rigorous error analysis for a numerical method applied to a stochastic PDE with rough noise, which is important for computational practitioners in stochastic PDEs.
The paper develops and analyzes a Crank-Nicolson finite element method for a linear stochastic heat equation with additive space-time white noise, providing strong error estimates for both the regularization and numerical approximation.
We formulate an initial- and Dirichlet boundary- value problem for a linear stochastic heat equation, in one space dimension, forced by an additive space-time white noise. First, we approximate the mild solution to the problem by the solution of the regularized second-order linear stochastic parabolic problem with random forcing proposed by Allen, Novosel and Zhang (Stochastics Stochastics Rep., 64, 1998). Then, we construct numerical approximations of the solution to the regularized problem by combining the Crank-Nicolson method in time with a standard Galerkin finite element method in space. We derive strong a priori estimates of the modeling error made in approximating the mild solution to the problem by the solution to the regularized problem, and of the numerical approximation error of the Crank-Nicolson finite element method.