Manifold sampling for data-driven UQ and optimization (Keynote lecture presented by R. Ghanem)

Abstract : We describe a new methodology for constructing probability measures from observations in high-dimensional space. A typical challenge with standard procedures for similar problems is the growth of the required number of samples with the dimension of the ambient space. The new methodology first delineates a manifold in a space spanned by available samples, then it constructs a probability distribution on that manifold together with a projected Ito equation for sampling from that distribution. A demonstration of this new methodology to problems in uncertainty quantification, and in design optimization under uncertainty will be shown.
Type de document :
Communication dans un congrès
USNCCM 2017, 14th U. S. National Congress on Computational Mechanics, Jul 2017, Montreal, Canada
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https://hal-upec-upem.archives-ouvertes.fr/hal-01566425
Contributeur : Christian Soize <>
Soumis le : vendredi 21 juillet 2017 - 09:26:18
Dernière modification le : vendredi 22 juin 2018 - 10:44:23

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  • HAL Id : hal-01566425, version 1

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Christian Soize, Roger Ghanem. Manifold sampling for data-driven UQ and optimization (Keynote lecture presented by R. Ghanem). USNCCM 2017, 14th U. S. National Congress on Computational Mechanics, Jul 2017, Montreal, Canada. 〈hal-01566425〉

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