Uniformly convergent adaptive methods for a class of parametric operator equations
ESAIM: Mathematical Modelling and Numerical Analysis , Tome 46 (2012) no. 6, pp. 1485-1508.

We derive and analyze adaptive solvers for boundary value problems in which the differential operator depends affinely on a sequence of parameters. These methods converge uniformly in the parameters and provide an upper bound for the maximal error. Numerical computations indicate that they are more efficient than similar methods that control the error in a mean square sense.

DOI : 10.1051/m2an/2012013
Classification : 35R60, 47B80, 65C20, 65N12, 65N22, 65J10
Mots-clés : parametric partial differential equations, partial differential equations with random coefficients, uniform convergence, adaptive methods, operator equations
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     title = {Uniformly convergent adaptive methods for a class of parametric operator equations},
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Gittelson, Claude Jeffrey. Uniformly convergent adaptive methods for a class of parametric operator equations. ESAIM: Mathematical Modelling and Numerical Analysis , Tome 46 (2012) no. 6, pp. 1485-1508. doi : 10.1051/m2an/2012013. http://archive.numdam.org/articles/10.1051/m2an/2012013/

[1] I. Babuška and P. Chatzipantelidis, On solving elliptic stochastic partial differential equations. Comput. Methods Appl. Mech. Eng. 191 (2002) 4093-4122. | MR | Zbl

[2] I.M. Babuška, R. Tempone and G.E. Zouraris, Galerkin finite element approximations of stochastic elliptic partial differential equations. SIAM J. Numer. Anal. 42 (2004) 800-825 (electronic). | MR | Zbl

[3] I.M. Babuška, F. Nobile and R. Tempone, A stochastic collocation method for elliptic partial differential equations with random input data. SIAM J. Numer. Anal. 45 (2007) 1005-1034 (electronic). | MR | Zbl

[4] A. Barinka, Fast Evaluation Tools for Adaptive Wavelet Schemes. Ph.D. thesis, RWTH Aachen (2005).

[5] M. Bieri and C. Schwab, Sparse high order FEM for elliptic sPDEs. Comput. Methods Appl. Mech. Eng. 198 (2009) 1149-1170. | MR | Zbl

[6] M. Bieri, R. Andreev and C. Schwab, Sparse tensor discretization of elliptic SPDEs. SIAM J. Sci. Comput. 31 (2009/2010) 4281-4304. | MR | Zbl

[7] P. Binev, W. Dahmen and R.A. Devore, Adaptive finite element methods with convergence rates. Numer. Math. 97 (2004) 219-268. | MR | Zbl

[8] A. Chkifa, A. Cohen, R. Devore and C. Schwab, Sparse adaptive Taylor approximation algorithms for parametric and stochastic elliptic PDEs. Technical Report 44, SAM, ETHZ (2011).

[9] A. Cohen, W. Dahmen and R.A. Devore, Adaptive wavelet methods for elliptic operator equations : convergence rates. Math. Comput. 70 (2001) 27-75 (electronic). | MR | Zbl

[10] A. Cohen, W. Dahmen and R.A. Devore, Adaptive wavelet methods. II. Beyond the elliptic case. Found. Comput. Math. 2 (2002) 203-245. | MR | Zbl

[11] A. Cohen, R.A. Devore and C. Schwab, Convergence rates of best -term Galerkin approximations for a class of elliptic sPDEs. Found. Comput. Math. 10 (2010) 615-646. | MR | Zbl

[12] A. Cohen, R. Devore and C. Schwab, Analytic regularity and polynomial approximation of parametric and stochastic elliptic PDE's. Anal. Appl. (Singap.) 9 (2011) 11-47. | MR | Zbl

[13] S. Dahlke, M. Fornasier and T. Raasch, Adaptive frame methods for elliptic operator equations. Adv. Comput. Math. 27 (2007) 27-63. | MR | Zbl

[14] S. Dahlke, T. Raasch, M. Werner, M. Fornasier and R. Stevenson, Adaptive frame methods for elliptic operator equations : the steepest descent approach. IMA J. Numer. Anal. 27 (2007) 717-740. | MR | Zbl

[15] M.K. Deb, I.M. Babuška and J.T. Oden, Solution of stochastic partial differential equations using Galerkin finite element techniques. Comput. Methods Appl. Mech. Eng. 190 (2001) 6359-6372. | MR | Zbl

[16] T.J. Dijkema, C. Schwab and R. Stevenson, An adaptive wavelet method for solving high-dimensional elliptic PDEs. Constr. Approx. 30 (2009) 423-455. | MR | Zbl

[17] W. Dörfler, A convergent adaptive algorithm for Poisson's equation. SIAM J. Numer. Anal. 33 (1996) 1106-1124. | MR | Zbl

[18] P. Frauenfelder, C. Schwab and R.A. Todor, Finite elements for elliptic problems with stochastic coefficients. Comput. Methods Appl. Mech. Eng. 194 (2005) 205-228. | MR | Zbl

[19] T. Gantumur, H. Harbrecht and R. Stevenson, An optimal adaptive wavelet method without coarsening of the iterands. Math. Comput. 76 (2007) 615-629 (electronic). | MR | Zbl

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

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

[22] C.J. Gittelson, Adaptive Galerkin Methods for Parametric and Stochastic Operator Equations. Ph.D. thesis, ETH Dissertation No. 19533. ETH Zürich (2011).

[23] C.J. Gittelson, An adaptive stochastic Galerkin method for random elliptic operators. Math. Comput. (2011). To appear. | MR | Zbl

[24] C.J. Gittelson, Convergence Rates of Multilevel and Sparse Tensor Approximations for a Random Elliptic PDE (2012). Submitted. | MR | Zbl

[25] I.G. Graham, F.Y. Kuo, D. Nuyens, R. Scheichl and I.H. Sloan, Quasi-Monte Carlo methods for elliptic PDEs with random coefficients and applications. J. Comput. Phys. 230 (2011) 3668-3694. | MR | Zbl

[26] R.V. Kadison and J.R. Ringrose, Fundamentals of the theory of operator algebras I, Elementary theory, Reprint of the 1983 original, in Graduate Studies in Mathematics. Amer. Math. Soc. 15 (1997). | MR | Zbl

[27] H.G. Matthies and A. Keese, Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations. Comput. Methods Appl. Mech. Eng. 194 (2005) 1295-1331. | MR | Zbl

[28] A. Metselaar, Handling Wavelet Expansions in Numerical Methods. Ph.D. thesis, University of Twente (2002). | MR

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

[30] F. Nobile, R. Tempone and C.G. Webster, An anisotropic sparse grid stochastic collocation method for partial differential equations with random input data. SIAM J. Numer. Anal. 46 (2008) 2411-2442. | MR | Zbl

[31] W. Rudin, Functional analysis, 2nd edition. International Series in Pure Appl. Math. McGraw-Hill Inc., New York (1991). | MR | Zbl

[32] C. Schwab and C.J. Gittelson, Sparse tensor discretization of high-dimensional parametric and stochastic PDEs. Acta Numer. 20 (2011) 291-467. | MR | Zbl

[33] R. Stevenson, Adaptive solution of operator equations using wavelet frames. SIAM J. Numer. Anal. 41 (2003) 1074-1100 (electronic). | MR | Zbl

[34] M.H. Stone, The generalized Weierstrass approximation theorem. Math. Mag. 21 (1948) 237-254. | MR | Zbl

[35] G. Szegő, Orthogonal polynomials, 4th edition, in Colloq. Publ. XXIII. Amer. Math. Soc. (1975).

[36] R.A. Todor and C. Schwab, Convergence rates for sparse chaos approximations of elliptic problems with stochastic coefficients. IMA J. Numer. Anal. 27 (2007) 232-261. | MR | Zbl

[37] X. Wan and G.E. Karniadakis, An adaptive multi-element generalized polynomial chaos method for stochastic differential equations. J. Comput. Phys. 209 (2005) 617-642. | MR | Zbl

[38] X. Wan and G.E. Karniadakis, Multi-element generalized polynomial chaos for arbitrary probability measures. SIAM J. Sci. Comput. 28 (2006) 901-928 (electronic). | MR | Zbl

[39] X. Wan and G.E. Karniadakis, Solving elliptic problems with non-Gaussian spatially-dependent random coefficients. Comput. Methods Appl. Mech. Eng. 198 (2009) 1985-1995. | MR | Zbl

[40] D. Xiu, Efficient collocational approach for parametric uncertainty analysis. Commun. Comput. Phys. 2 (2007) 293-309. | MR | Zbl

[41] D. Xiu, Numerical methods for stochastic computations : A spectral method approach. Princeton University Press, Princeton, NJ (2010). | MR | Zbl

[42] D. Xiu and J.S. Hesthaven, High-order collocation methods for differential equations with random inputs. SIAM J. Sci. Comput. 27 (2005) 1118-1139 (electronic). | MR | Zbl

[43] D. Xiu and G.E. Karniadakis, The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24 (2002) 619-644 (electronic). | MR | Zbl

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