A convergent adaptive stochastic Galerkin finite element method with quasi-optimal spatial meshes
ESAIM: Mathematical Modelling and Numerical Analysis , Tome 49 (2015) no. 5, pp. 1367-1398.

We analyze a posteriori error estimation and adaptive refinement algorithms for stochastic Galerkin Finite Element methods for countably-parametric, elliptic boundary value problems. A residual error estimator which separates the effects of gpc-Galerkin discretization in parameter space and of the Finite Element discretization in physical space in energy norm is established. It is proved that the adaptive algorithm converges. To this end, a contraction property of its iterates is proved. It is shown that the sequences of triangulations which are produced by the algorithm in the FE discretization of the active gpc coefficients are asymptotically optimal. Numerical experiments illustrate the theoretical results.

Reçu le :
DOI : 10.1051/m2an/2015017
Classification : 65N30, 35R60, 47B80, 60H35, 65C20, 65N12, 65N22, 65J10
Mots-clés : Partial differential equations with random coefficients, generalized polynomial chaos, adaptive finite element methods, contraction property, residuala posteriori error estimation, uncertainty quantification
Eigel, Martin 1 ; Gittelson, Claude Jeffrey 2 ; Schwab, Christoph 3 ; Zander, Elmar 4

1 Weierstrass Institute, Mohrenstrasse 39, 10117 Berlin, Germany.
2 Department of Mathematics, Purdue University, 150 N. University Street, West Lafayette, IN 47907-2067, USA.
3 Seminar for Applied Mathematics, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland.
4 Institute of Scientific Computing, Technical University Braunschweig, 38092 Braunschweig, Germany.
@article{M2AN_2015__49_5_1367_0,
     author = {Eigel, Martin and Gittelson, Claude Jeffrey and Schwab, Christoph and Zander, Elmar},
     title = {A convergent adaptive stochastic {Galerkin} finite element method with quasi-optimal spatial meshes},
     journal = {ESAIM: Mathematical Modelling and Numerical Analysis },
     pages = {1367--1398},
     publisher = {EDP-Sciences},
     volume = {49},
     number = {5},
     year = {2015},
     doi = {10.1051/m2an/2015017},
     mrnumber = {3423228},
     zbl = {1335.65006},
     language = {en},
     url = {http://www.numdam.org/articles/10.1051/m2an/2015017/}
}
TY  - JOUR
AU  - Eigel, Martin
AU  - Gittelson, Claude Jeffrey
AU  - Schwab, Christoph
AU  - Zander, Elmar
TI  - A convergent adaptive stochastic Galerkin finite element method with quasi-optimal spatial meshes
JO  - ESAIM: Mathematical Modelling and Numerical Analysis 
PY  - 2015
SP  - 1367
EP  - 1398
VL  - 49
IS  - 5
PB  - EDP-Sciences
UR  - http://www.numdam.org/articles/10.1051/m2an/2015017/
DO  - 10.1051/m2an/2015017
LA  - en
ID  - M2AN_2015__49_5_1367_0
ER  - 
%0 Journal Article
%A Eigel, Martin
%A Gittelson, Claude Jeffrey
%A Schwab, Christoph
%A Zander, Elmar
%T A convergent adaptive stochastic Galerkin finite element method with quasi-optimal spatial meshes
%J ESAIM: Mathematical Modelling and Numerical Analysis 
%D 2015
%P 1367-1398
%V 49
%N 5
%I EDP-Sciences
%U http://www.numdam.org/articles/10.1051/m2an/2015017/
%R 10.1051/m2an/2015017
%G en
%F M2AN_2015__49_5_1367_0
Eigel, Martin; Gittelson, Claude Jeffrey; Schwab, Christoph; Zander, Elmar. A convergent adaptive stochastic Galerkin finite element method with quasi-optimal spatial meshes. ESAIM: Mathematical Modelling and Numerical Analysis , Tome 49 (2015) no. 5, pp. 1367-1398. doi : 10.1051/m2an/2015017. http://www.numdam.org/articles/10.1051/m2an/2015017/

P. Binev, W. Dahmen and R. Devore, Adaptive finite element methods with convergence rates. Numer. Math. 97 (2004) 219–268. | DOI | MR | Zbl

J.M. Cascon, C. Kreuzer, R.H. Nochetto and K.G. Siebert, Quasi-optimal convergence rate for an adaptive finite element method. SIAM J. Numer. Anal. 46 (2008) 2524–2550. | DOI | MR | Zbl

A. Chkifa, A. Cohen, R. Devore and C. Schwab, Adaptive algorithms for sparse polynomial approximation of parametric and stochastic elliptic pdes. ESAIM: M2AN 47 (2013) 253–280. | DOI | Numdam | MR | Zbl

A. Chkifa, A. Cohen and C. Schwab, High-dimensional adaptive sparse polynomial interpolation and applications to parametric pdes. J. Found. Comput. Math. 14 (2014) 601–633. | DOI | MR | Zbl

A. Cohen, R. Devore and C. Schwab, Analytic regularity and polynomial approximation of parametric and stochastic elliptic PDE’s. Anal. Appl. 9 (2011) 11–47. | DOI | MR | Zbl

W. Dörfler, A convergent adaptive algorithm for Poisson’s equation. SIAM J. Numer. Anal. 33 (1996) 1106–1124. | DOI | MR | Zbl

M. Eigel, C. Gittelson, C. Schwab and E. Zander, Adaptive stochastic Galerkin FEM. Comput. Methods Appl. Mech. Engrg. 270 (2014) 247–269. | DOI | MR | Zbl

M. Eigel and E. Zander, ALEA - A Python Framework for Spectral Methods and Low-Rank Approximations in Uncertainty Quantification. Available at: https://bitbucket.org/aleadev/alea.

W. Gautschi, Orthogonal polynomials: computation and approximation. Numer. Math. Sci. Comput. Oxford University Press, New York (2004). | MR | Zbl

R.G. Ghanem and P.D. Spanos, Stochastic finite elements: a spectral approach. Springer-Verlag, New York (1991). | MR | Zbl

C.J. Gittelson, R. Andreev and Ch. Schwab, Optimality of adaptive Galerkin methods for random parabolic partial differential equations. J. Comput. Appl. Math. 263 (2014) 189–201. | DOI | MR | Zbl

C.J. Gittelson, Stochastic Galerkin approximation of operator equations with infinite dimensional noise. Tech. Report 2011-10. Seminar for Applied Mathematics, ETH Zürich (2011).

C.J. Gittelson, Convergence rates of multilevel and sparse tensor approximations for a random elliptic PDE. SIAM J. Numer. Anal. 51 (2013) 2426–2447. | DOI | MR | Zbl

C.J. Gittelson, High-order methods as an alternative to using sparse tensor products for stochastic galerkin FEM. Comput. Math. Appl. 67 (2014) 888-898. | DOI | MR | Zbl

F.Y. Kuo, C. Schwab and I.H. Sloan, Quasi-monte carlo finite element methods for a class of elliptic partial differential equations with random coefficients. SIAM J. Numer. Anal. 50 (2012) 3351–3374. | DOI | MR | Zbl

O.P. Le Maître and O.M. Knio, Spectral methods for uncertainty quantification, Scientific Computation. Springer, New York (2010).With applications to computational fluid dynamics. | MR | Zbl

P. Morin, R.H. Nochetto and K.G. Siebert, Data oscillation and convergence of adaptive FEM. SIAM J. Numer. Anal. 38 (2000) 466–488. | DOI | MR | Zbl

R.H. Nochetto, K.G. Siebert and A. Veeser, Theory of adaptive finite element methods: an introduction, in Multiscale, Nonlinear and Adaptive Approximation. Springer, Berlin (2009) 409–542. | MR | Zbl

C. Schillings and Ch. Schwab Sparse, adaptive Smolyak quadratures for Bayesian inverse problems. Inverse Probl. 29 (2013) 065011. | DOI | MR | Zbl

R. Stevenson, The completion of locally refined simplicial partitions created by bisection. Math. Comput. (2008) 227–241. | MR | Zbl

R. Verfürth, A Review of a Posteriori Error Estimation and Adaptive Mesh-Refinement Techniques. Teubner Verlag and J. Wiley, Stuttgart (1996). | Zbl

Cité par Sources :