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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.
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https://hal-upec-upem.archives-ouvertes.fr/hal-01566425
Contributor : Christian Soize <>
Submitted on : Friday, July 21, 2017 - 9:26:18 AM
Last modification on : Thursday, March 19, 2020 - 11:52:04 AM

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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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