Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization
Model order reduction; Computational homogenisation; Multiscale
Abstract :
[en] In this paper, we present new reliable model order reduction strategies for computational micromechanics. The difficulties rely mainly upon the high dimensionality of the parameter space represented by any load path applied onto the representative volume element. We take special care of the challenge of selecting an exhaustive snapshot set. This is treated by first using a random sampling of energy dissipating load paths and then in a more advanced way using Bayesian optimization associated with an interlocked division of the parameter space. Results show that we can insure the selection of an exhaustive snapshot set from which a reliable reduced-order model can be built.
Disciplines :
Materials science & engineering
Author, co-author :
Goury, Olivier; Cardiff University > School of Engineering
Amsallem, David; Stanford University > Department of Aeronautics and Astronautics
Bordas, Stéphane ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Liu, Wing Kam; Northwestern University > Department of Mechanical Engineering
Kerfriden, Pierre; Cardiff University > School of Engineering
External co-authors :
yes
Language :
English
Title :
Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization
Publication date :
April 2016
Journal title :
Computational Mechanics
ISSN :
1432-0924
Publisher :
Springer Science & Business Media B.V., New York, United States - New York