Mechanical State Estimation with a Polynomial-Chaos-Based Statistical Finite Element Method

TL;DR

This paper introduces a sampling-free statistical finite element method using Polynomial Chaos expansion for efficient Bayesian state estimation in structural mechanics, accommodating non-Gaussian uncertainties and model errors.

Contribution

It develops a novel Polynomial Chaos-based statFEM that handles non-Gaussian priors and non-conjugate likelihoods without sampling, improving computational efficiency.

Findings

Demonstrates efficiency in 1D and 2D elastostatic problems.

Shows convergence and accuracy of the proposed method.

Handles non-Gaussian and non-stationary uncertainties effectively.

Abstract

The Statistical Finite Element Method (statFEM) offers a Bayesian framework for integrating computational models with observational data, thus providing improved predictions for structural health monitoring and digital twinning. This paper presents an efficient sampling-free statFEM tailored for non-conjugate, non-Gaussian prior probability densities. We assume that constitutive parameters, modeled as weakly stationary random fields, are the primary source of uncertainty and approximate them using Karhunen-Lo\`eve (KL) expansion. The resulting stochastic solution field, i.e., the displacement field, is a non-stationary, non-Gaussian random field, which we approximate via Polynomial Chaos (PC) expansion. The PC coefficients are determined through projection using Smolyak sparse grids. Additionally, we model the measurement noise as a stationary Gaussian random field and the model misspecification as a mean-free, non-stationary Gaussian random field, which is also approximated using KL expansion. The coefficients of the KL expansion are treated as hyperparameters. The PC coefficients of the stochastic posterior displacement field are computed using the Gauss-Markov-K\'alm\'an filter, while the hyperparameters are determined by maximizing the marginal likelihood. We demonstrate the efficiency and convergence of the proposed method through one- and two-dimensional elastostatic problems.