Statistical finite elements for sequential data synthesis in solid dynamics
For researchers in computational mechanics, this provides a framework to assimilate data into dynamic finite element models while quantifying uncertainties, though it is an incremental extension of existing statFEM work.
This paper extends the statistical finite element method (statFEM) to synthesize observational data with elastodynamic finite element models, using Bayesian filtering to account for uncertainties. The method is demonstrated on 1D and 2D examples with synthetic data, but no concrete performance numbers are reported.
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.