Bordas, Stéphane[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
23-Jul-2018
No
International
13th World Congress in Computational Mechanics (WCCM XIII)
from 22-07-2018 to 27-07-2018
[en] Monte Carlo ; Uncertainty quantification ; Biomechanics
[en] We present probabilistic approaches aiming at the selection of the best constitutive model and to identify their parameters from experimental data. These parameters are always associated with some degree of uncertainty. It is therefore important to study how this statistical uncertainty in parameters propagates to a safety-critical quantity of interest in the output of a model. Efficient Monte Carlo methods based on variance reduction techniques (Sensitivity Derivatives Monte Carlo methods [Hauseux et al. 2017] and MultiLevel Monte Carlo [Giles 2015] methods) are employed to propagate this uncertainty for both random variables and random fields. Inverse and forward problems are strongly connected. In a bayesian setting [Matthies et al. 2017], developing methods that reduce the number of evaluations of the forward model to an absolute minimum to achieve convergence is crucial for tractable computations. Numerical results in the context of soft tissue biomechanics are presented and discussed.
[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
