STRONG CONVERGENCE OF A FULLY DISCRETE FINITE ELEMENT APPROXIMATION OF THE STOCHASTIC CAHN-HILLIARD EQUATION

We consider the stochastic Cahn-Hilliard equation driven by additive Gaussian noise in a convex domain with polygonal boundary in dimension d <= 3. We discretize the equation using a standard finite element method in space and a fully implicit backward Euler method in time. By proving optimal error estimates on subsets of the probability space with arbitrarily large probability and uniform-in-time moment bounds we show that the numerical solution converges strongly to the solution as the discretization parameters tend to zero.