Reduced basis techniques for stochastic problems
Author(s)
Boyaval, S.; Le Bris, C.; Lelievre, T.; Maday, Yvon; Nguyen, Ngoc Cuong; Patera, Anthony T.; ... Show more Show less
DownloadPatera_Reduced basis.pdf (396.5Kb)
OPEN_ACCESS_POLICY
Open Access Policy
Creative Commons Attribution-Noncommercial-Share Alike
Terms of use
Metadata
Show full item recordAbstract
We report here on the recent application of a now classical general reduction technique, the Reduced-Basis (RB) approach initiated by C. Prud’homme et al. in J. Fluids Eng. 124(1), 70–80, 2002, to the specific context of differential equations with random coefficients. After an elementary presentation of the approach, we review two contributions of the authors: in Comput. Methods Appl. Mech. Eng. 198(41–44), 3187–3206, 2009, which presents the application of the RB approach for the discretization of a simple second order elliptic equation supplied with a random boundary condition, and in Commun. Math. Sci., 2009, which uses a RB type approach to reduce the variance in the Monte-Carlo simulation of a stochastic differential equation. We conclude the review with some general comments and also discuss possible tracks for further research in the direction.
Date issued
2010-10Department
Massachusetts Institute of Technology. Department of Mechanical EngineeringJournal
Archives of Computational Methods in Engineering
Publisher
International Center for Numerical Methods in Engineering
Citation
Boyaval, S. et al. “Reduced Basis Techniques for Stochastic Problems.” Archives of Computational Methods in Engineering 17.4 (2010) : 435-454-454.
Version: Author's final manuscript
ISSN
1134-3060
1886-1784