Manifold sampling for data-driven UQ and optimization (Keynote lecture presented by R. Ghanem) - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

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

Résumé

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.
Fichier non déposé

Dates et versions

hal-01566425 , version 1 (21-07-2017)

Identifiants

  • HAL Id : hal-01566425 , version 1

Citer

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⟩
230 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More