The Bayesian Finite Element Method in Inverse Problems: a Critical Comparison between Probabilistic Models for Discretization Error

TL;DR

This paper evaluates the Bayesian finite element method (BFEM) for inverse problems, demonstrating its superior ability to accurately quantify and propagate discretization uncertainty compared to other methods, leading to more reliable parameter estimates.

Contribution

The work provides a comprehensive comparison of BFEM with RM-FEM and statFEM, highlighting BFEM's robustness and structural advantages in uncertainty propagation for inverse problems.

Findings

BFEM produces more accurate parameter estimates.

BFEM prevents overconfidence in posterior estimates.

BFEM outperforms RM-FEM and statFEM in uncertainty propagation.

Abstract

When using the finite element method (FEM) in inverse problems, its discretization error can produce parameter estimates that are inaccurate and overconfident. The Bayesian finite element method (BFEM) provides a probabilistic model for the epistemic uncertainty due to discretization error. In this work, we apply BFEM to various inverse problems, and compare its performance to the random mesh finite element method (RM-FEM) and the statistical finite element method (statFEM), which serve as a frequentist and inference-based counterpart to BFEM. We find that by propagating this uncertainty to the posterior, BFEM can produce more accurate parameter estimates and prevent overconfidence, compared to FEM. Because the BFEM covariance operator is designed to leave uncertainty only in the appropriate space, orthogonal to the FEM basis, BFEM is able to outperform RM-FEM, which does not have such a structure to its covariance. Although inferring the discretization error via a model misspecification component is possible as well, as is done in statFEM, the feasibility of such an approach is contingent on the availability of sufficient data. We find that the BFEM is the most robust way to consistently propagate uncertainty due to discretization error to the posterior of a Bayesian inverse problem.