Adaptive Quasi-Monte Carlo Finite Element Methods for Parametric Elliptic PDEs

We introduce novel adaptive methods to approximate moments of solutions of partial differential Equations (PDEs) with uncertain parametric inputs. A typical problem in Uncertainty Quantification is the approximation of the expected values of quantities of interest of the solution, which requires the efficient numerical approximation of high-dimensional integrals. We perform this task by a class of deterministic quasi-Monte Carlo integration rules derived from Polynomial lattices, that allows to control a-posteriori the integration error without querying the governing PDE and does not incur the curse of dimensionality. Based on an abstract formulation of adaptive finite element methods (AFEM) for deterministic problems, we infer convergence of the combined adaptive algorithms in the parameter and physical space. We propose a selection of examples of PDEs admissible for these algorithms. Finally, we present numerical evidence of convergence for a model diffusion PDE.