Publication de 6 numéros par an
ISSN Imprimer: 2152-5080
ISSN En ligne: 2152-5099
Indexed in
FAST AND ACCURATE MODEL REDUCTION FOR SPECTRAL METHODS IN UNCERTAINTY QUANTIFICATION
RÉSUMÉ
A fast and accurate model order reduction procedure is presented that can successfully be applied to spectral methods for uncertainty quantification problems. The main novelties include (1) the application of model order reduction to uncertainty quantification problems; (2) the improvement of existing model order reduction methods in order to meet the accuracy and performance requirements; and (3) an efficient approach for systems with many outputs. Numerical experiments for large-scale realistic systems illustrate the suitability and performance (50× speedup while preserving accuracy) for uncertainty quantification problems.
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Ullmann Sebastian, Müller Christopher, Lang Jens, Stochastic Galerkin Reduced Basis Methods for Parametrized Linear Convection–Diffusion–Reaction Equations, Fluids, 6, 8, 2021. Crossref
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Kollepara Kiran Sagar, Navarro‐Jiménez José M., Le Guennec Yves, Silva Luisa, Aguado José V., On the limitations of low‐rank approximations in contact mechanics problems, International Journal for Numerical Methods in Engineering, 2022. Crossref