Statistical finite elements for sequential data synthesis in solid dynamics

TL;DR

This paper introduces a Bayesian statistical finite element method for synthesizing observational data in elastodynamics, accounting for uncertainties and model discrepancies through probabilistic filtering and Gaussian random fields.

Contribution

It extends the statFEM framework with a Bayesian filtering approach and stochastic PDE-based random fields, providing a new way to incorporate uncertainties in dynamic solid mechanics modeling.

Findings

Successfully applied to 1D and 2D elastodynamic examples with synthetic data.

Calibrated model misspecification hyperparameters via marginal likelihood maximization.

Produced Gaussian posteriors with closed-form mean and covariance for the state.

Abstract

We present an approach for synthesising observational data with elastodynamic finite element models by extending the statistical finite element method (statFEM) framework. The proposed formulation adopts a Bayesian filtering approach to account for uncertainties in the data, the finite element model, and the discrepancies between the model and the physical system. Observational data are assimilated while the state of the spatially discretised finite element problem is advanced in time using the stochastic variant of the explicit Newmark scheme. The prior probability density of the state is obtained by solving an incremental probabilistic forward problem, and the corresponding posterior density is obtained by conditioning on the data available at each time step. In the probabilistic forward problem, spatio-temporal Gaussian random fields representing the forcing, model misspecification, and material parameters are specified via their stochastic PDE formulation. The resulting non-Gaussian prior probability density is approximated using a perturbation approach, yielding a Gaussian posterior with closed-form mean and covariance. The hyperparameters of the random field representing model misspecification are calibrated by maximising the marginal likelihood of the data. The proposed approach is illustrated on one- and two-dimensional elastodynamic examples with synthetic data.