Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems

Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami

Abstract: This paper develops meshless methods for probabilistically describing discretisation error in the numerical solution of partial differential equations. This construction enables the solution of Bayesian inverse problems while accounting for the impact of the discretisation of the forward problem. In particular, this drives statistical inferences to be more conservative in the presence of significant solver error. Theoretical results are presented describing rates of convergence for the posteriors in both the forward and inverse problems. This method is tested on a challenging inverse problem with a nonlinear forward model.