Bordas, Stéphane[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
2016
1
No
International
2016 European Congress on Computational Methods in Applied Sciences and Engineering
5-6-2016 to 10-6-2016
ECCOMAS
Crete
Greece
[en] We present a method for calculating a Bayesian uncertainty estimate on the recovered material parameters of a heterogeneous geometrically non-linear hyperelastic body. We formulate the problem in the Bayesian inference framework [1]; given noisy and sparse observations of a body, some prior knowledge on the parameters and a parameter-to-observable map the goal is to recover the posterior distribution of the parameters given the observations. In this work we primarily focus on the challenges of developing dimension-independent algorithms in the context of very large inverse problems (tens to hundreds of thousands of parameters). Critical to the success of the method is viewing the problem in the correct infinite- dimensional function space setting [2]. With this goal in mind, we show the use of automatic symbolic differentiation techniques to construct high-order adjoint models [3], scalable maximum a posteriori (MAP) estimators, and efficient low-rank update methods to calculate credible regions on the posterior distribution [4].
FP7 ; 279578 - REALTCUT - Towards real time multiscale simulation of cutting in non-linear materials with applications to surgical simulation and computer guided surgery
FnR ; FNR6693582 > Jack Samuel Hale > > Advanced Computational Methods for the Simulation of Cutting in Surgery > 01/01/2014 > 31/12/2015 > 2013
[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
