Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization

Olivier Goury*, David Amsallem, Stéphane Pierre Alain Bordas, Wing Kam Liu, Pierre Kerfriden

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)213-234
Number of pages22
JournalComputational Mechanics
Volume58
Issue number2
DOIs
StatePublished - Aug 1 2016

Keywords

  • Computational homogenisation
  • Damage mechanics
  • Hyperreduction
  • Model order reduction
  • Multiscale
  • Reduced basis

ASJC Scopus subject areas

  • Computational Mechanics
  • Ocean Engineering
  • Mechanical Engineering
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

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