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 …