A Bayesian filtering extension of statFEM assimilates sequential observational data into elastodynamic finite element models by modeling uncertainties as Gaussian random fields, advancing the state with a stochastic Newmark scheme, and approximating the non-Gaussian prior via perturbation to obtain,
Grønbech-Jensen, Complete set of stochastic Verlet-type thermostats for correct Langevin simulations, Molecular Physics 118 (8) (2020) e1662506, ISSN 0026-8976
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Statistical finite elements for sequential data synthesis in solid dynamics
A Bayesian filtering extension of statFEM assimilates sequential observational data into elastodynamic finite element models by modeling uncertainties as Gaussian random fields, advancing the state with a stochastic Newmark scheme, and approximating the non-Gaussian prior via perturbation to obtain,