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International Journal for Uncertainty Quantification
Импакт фактор: 4.911 5-летний Импакт фактор: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN Печать: 2152-5080
ISSN Онлайн: 2152-5099

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International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2016016646
pages 271-286

FAST AND ACCURATE MODEL REDUCTION FOR SPECTRAL METHODS IN UNCERTAINTY QUANTIFICATION

Francisco Damascene Freitas
Department of Electrical Engineering, University of Brasilia, CEP: 70910-900, Brasilia, DF, Brazil
Roland Pulch
Institute for Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str. 47, D-17489 Greifswald, Germany
Joost Rommes
Mentor Graphics, 110 rue Blaise Pascal, Montbonnot, France

Краткое описание

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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