An IMEX Finite Element Method for a linearized Cahn-Hilliard-Cook equation driven by the space derivative of a space-time white noise
Provides rigorous numerical analysis for a stochastic PDE with rough noise, relevant to researchers in computational stochastic PDEs.
The paper develops a numerical method for a linearized Cahn-Hilliard-Cook equation driven by space-time white noise, proving strong error estimates for the approximation of the mild solution.
We consider a model initial- and Dirichlet boundary- value problem for a linearized Cahn-Hilliard-Cook equation, in one space dimension, forced by the space derivative of a space-time white noise. First, we introduce a canvas problem the solution to which is a regular approximation of the mild solution to the problem and depends on a finite number of random variables. Then, fully-discrete approximations of the solution to the canvas problem are constructed using, for discretization in space, a Galerkin finite element method based on $H^2$ piecewise polynomials, and, for time-stepping, an implicit/explicit method. Finally, we derive a strong a priori estimate of the error approximating the mild solution to the problem by the canvas problem solution, and of the numerical approximation error of the solution to the canvas problem.