Reduced Basis Techniques for Stochastic Problems

被引:97
作者
Boyaval, S. [1 ,2 ]
Le Bris, C. [1 ,2 ]
Lelievre, T. [1 ,2 ]
Maday, Y. [3 ,4 ]
Nguyen, N. C. [5 ]
Patera, A. T. [5 ]
机构
[1] Univ Paris Est, CERMICS, Ecole Ponts ParisTech, F-77455 Cite Descartes 2, Marne Vallee, France
[2] INRIA, MICMAC Project Team, F-78153 Le Chesnay, France
[3] Univ Paris 06, UMR 7598, Lab Jacques Louis Lions, F-75005 Paris, France
[4] Brown Univ, Div Appl Math, Providence, RI 02912 USA
[5] MIT, Dept Mech Engn, Cambridge, MA 02139 USA
关键词
PARTIAL-DIFFERENTIAL-EQUATIONS; POSTERIORI ERROR ESTIMATION; SOLID MECHANICS HELD; BASIS APPROXIMATION; FUNCTIONAL QUANTIZATION; VARIANCE REDUCTION; WASHINGTON; BEHAVIOR; OCTOBER; FIELDS;
D O I
10.1007/s11831-010-9056-z
中图分类号
TP39 [计算机的应用];
学科分类号
080201 [机械制造及其自动化];
摘要
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.
引用
收藏
页码:435 / 454
页数:20
相关论文
共 85 条
[1]
Ainsworth M., 2000, PUR AP M-WI
[2]
AUTOMATIC CHOICE OF GLOBAL SHAPE FUNCTIONS IN STRUCTURAL-ANALYSIS [J].
ALMROTH, BO ;
STERN, P ;
BROGAN, FA .
AIAA JOURNAL, 1978, 16 (05) :525-528
[3]
[Anonymous], ANN ACAD SCI FENN
[4]
[Anonymous], 1964, Monte Carlo Methods, DOI DOI 10.1007/978-94-009-5819-7
[5]
[Anonymous], MIT PAPPALA IN PRESS
[6]
Arouna B., 2004, Journal of Computational Finance, V7, P35
[7]
Solving elliptic boundary value problems with uncertain coefficients by the finite element method: the stochastic formulation [J].
Babuska, I ;
Tempone, R ;
Zouraris, GE .
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2005, 194 (12-16) :1251-1294
[8]
A stochastic collocation method for elliptic partial differential equations with random input data [J].
Babuska, Ivo ;
Nobile, Fabio ;
Tempone, Raul .
SIAM JOURNAL ON NUMERICAL ANALYSIS, 2007, 45 (03) :1005-1034
[9]
An 'empirical interpolation' method: application to efficient reduced-basis discretization of partial differential equations [J].
Barrault, M ;
Maday, Y ;
Nguyen, NC ;
Patera, AT .
COMPTES RENDUS MATHEMATIQUE, 2004, 339 (09) :667-672
[10]
ON THE REDUCED BASIS METHOD [J].
BARRETT, A ;
REDDIEN, G .
ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, 1995, 75 (07) :543-549