Sparse grid approximation of the stochastic Landau-Lifshitz-Gilbert equation

We show convergence rates for a sparse grid approximation of the distribution of solutions of the stochastic Landau-Lifshitz-Gilbert equation. Beyond being a frequently studied equation in engineering and physics, the stochastic Landau-Lifshitz-Gilbert equation poses many interesting challenges that do not appear simultaneously in previous works on uncertainty quantification: The equation is strongly nonlinear, time-dependent, and has a non-convex side constraint. Moreover, the parametrization of the stochastic noise features countably many unbounded parameters and low regularity compared to other elliptic and parabolic problems studied in uncertainty quantification. We use a novel technique to establish uniform holomorphic regularity of the parameter-to-solution map based on a Gronwall-type estimate combined with previously known methods that use the implicit function theorem. We demonstrate numerically the feasibility of the stochastic collocation method and show a clear advantage of a multi-level stochastic collocation scheme for the stochastic Landau-Lifshitz-Gilbert equation.