Using higher-order adjoints to accelerate the solution of UQ problems with random fields
English
Hale, Jack[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Hauseux, Paul[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Bordas, Stéphane[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
8-Jan-2018
A0
No
No
International
Key UQ methodologies and motivating applications
8-1-2018 to 12-1-2018
Isaac Newton Institute for Mathematical Sciences
Cambridge
United Kingdom
[en] monte carlo ; variance reduction ; uncertainty quantification ; taylor series ; FEniCS ; PETSc ; DOLFIN
[en] A powerful Monte Carlo variance reduction technique introduced in Cao and Zhang 2004 uses
local derivatives to accelerate Monte Carlo estimation. This work aims to: develop a new derivative-driven estimator that works for SPDEs with uncertain data modelled as Gaussian random fields with Matérn covariance functions (infinite/high-dimensional problems) (Lindgren, Rue, and Lindström, 2011), use second-order derivative (Hessian) information for improved variance reduction over our approach in (Hauseux, Hale, and Bordas, 2017), demonstrate a software framework using FEniCS (Logg and Wells, 2010), dolfin-adjoint (Farrell et al., 2013) and PETSc (Balay et al., 2016) for automatic acceleration of MC estimation for a wide variety of PDEs on HPC architectures.
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 > ACCeSS > Advanced Computational Methods For The Simulation Of Cutting In Surgery > 01/01/2014 > 31/12/2015 > 2013