Error analysis for a statistical finite element method

Abstract

The recently proposed statistical finite element (statFEM) approach synthesises measurement data with finite element models and allows for making predictions about the true system response. We provide a probabilistic error analysis for a prototypical statFEM setup based on a Gaussian process prior under the assumption that the noisy measurement data are generated by a deterministic true system response function that satisfies a second-order elliptic partial differential equation for an unknown true source term. In certain cases, properties such as the smoothness of the source term may be misspecified by the Gaussian process model. The error estimates we derive are for the expectation with respect to the measurement noise of the L 2 -norm of the difference between the true system response and the mean of the statFEM posterior. The estimates imply polynomial rates of convergence in the numbers of measurement points and finite element basis functions and depend on the Sobolev smoothness of the true source term and the Gaussian process model. A numerical example for Poisson's equation is used to illustrate these theoretical results.

Mesh-Free MLS-Based Error-Recovery Technique for Finite Element Incompressible Elastic Computations

“…The application of the MLS-based mesh-less recovery technique for gradient-error recovery in elastic analysis was proposed by Ahmed et al [30]. Karvonen et al [31] performed a probabilistic error analysis on a Gaussian-process-based statistical finite element method (statFEM). The adaptive simulation of magnetized plasma transport in fusion reactors was carried out by Vogl et al [32].…”