Linear collective collocation approximation for parametric and stochastic elliptic PDEs
Sbornik. Mathematics, Tome 210 (2019) no. 4, pp. 565-588 Cet article a éte moissonné depuis la source Math-Net.Ru

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Consider the parametric elliptic problem $$ -\operatorname{div}\bigl(a(y)(x)\nabla u(y)(x)\bigr)=f(x),\qquad x\in D,\quad y\in\mathbb I^\infty,\quad u|_{\partial D}=0, $$ where $D\subset\mathbb R^m$ is a bounded Lipschitz domain, $\mathbb I^\infty:=[-1,1]^\infty$, $f\in L_2(D)$, and the diffusion coefficients $a$ satisfy the uniform ellipticity assumption and are affinely dependent on $y$. The parameter $y$ can be interpreted as either a deterministic or a random variable. A central question to be studied is as follows. Assume that there is a sequence of approximations with a certain error convergence rate in the energy norm of the space $V:=H^1_0(D)$ for the nonparametric problem $-\operatorname{div}\bigl(a(y_0)(x)\nabla u(y_0)(x)\bigr)=f(x)$ at every point $y_0\in\mathbb I^\infty$. Then under what assumptions does this sequence induce a sequence of approximations with the same error convergence rate for the parametric elliptic problem in the norm of the Bochner spaces $L_\infty(\mathbb I^\infty,V)$? We have solved this question using linear collective collocation methods, based on Lagrange polynomial interpolation on the parametric domain $\mathbb I^\infty$. Under very mild conditions, we show that these approximation methods give the same error convergence rate as for the nonparametric elliptic problem. In this sense the curse of dimensionality is broken by linear methods. Bibliography: 22 titles.
Keywords: high-dimensional problems, parametric and stochastic elliptic PDEs, linear collective collocation approximation
Mots-clés : affine dependence of the diffusion coefficients.
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     author = {Dinh D\~{u}ng},
     title = {Linear collective collocation approximation for parametric and stochastic elliptic {PDEs}},
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     url = {http://geodesic.mathdoc.fr/item/SM_2019_210_4_a4/}
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Dinh Dũng. Linear collective collocation approximation for parametric and stochastic elliptic PDEs. Sbornik. Mathematics, Tome 210 (2019) no. 4, pp. 565-588. http://geodesic.mathdoc.fr/item/SM_2019_210_4_a4/

[1] I. Babuška, F. Nobile, R. Tempone, “A stochastic collocation method for elliptic partial differential equations with random input data”, SIAM J. Numer. Anal., 45:3 (2007), 1005–1034 | DOI | MR | Zbl

[2] M. Bachmayr, A. Cohen, G. Migliorati, “Sparse polynomial approximation of parametric elliptic PDEs. Part I: affine coefficients”, ESAIM Math. Model. Numer. Anal., 51:1 (2017), 321–339 | DOI | MR | Zbl

[3] M. Bachmayr, A. Cohen, Dinh Dũng, Ch. Schwab, “Fully discrete approximation of parametric and stochastic elliptic PDEs”, SIAM J. Numer. Anal., 55:5 (2017), 2151–2186 | DOI | MR | Zbl

[4] J. Beck, R. Tempone, F. Nobile, L. Tamellini, “On the optimal polynomial approximation of stochastic PDEs by Galerkin and collocation methods”, Math. Models Methods Appl. Sci., 22:9 (2012), 1250023, 33 pp. ; MOX rep. 23/2011, Politechnico di Milano, Milano, 2011, 38 pp. https://mox.polimi.it/publication-results/?id=2998&tipo=add_qmox | DOI | MR | Zbl

[5] J. Céa, “Approximation variationnelle des problèmes aux limites”, Ann. Inst. Fourier (Grenoble), 14:2 (1964), 345–444 | DOI | MR | Zbl

[6] M. A. Chkifa, “On the Lebesgue constant of Leja sequences for the complex unit disk and of their real projection”, J. Approx. Theory, 166 (2013), 176–200 | DOI | MR | Zbl

[7] A. Chkifa, A. Cohen, R. DeVore, Ch. Schwab, “Sparse adaptive Taylor approximation algorithms for parametric and stochastic elliptic PDEs”, ESAIM Math. Model. Numer. Anal., 47:1 (2013), 253–280 | DOI | MR | Zbl

[8] A. Chkifa, A. Cohen, Ch. Schwab, “High-dimensional adaptive sparse polynomial interpolation and applications to parametric PDEs”, Found. Comput. Math., 14:4 (2014), 601–633 | DOI | MR | Zbl

[9] A. Cohen, R. DeVore, “Approximation of high-dimensional parametric PDEs”, Acta Numer., 24 (2015), 1–159 | DOI | MR | Zbl

[10] A. Cohen, R. DeVore, Ch. Schwab, “Convergence rates of best $N$-term Galerkin approximations for a class of elliptic sPDEs”, Found. Comput. Math., 10:6 (2010), 615–646 | DOI | MR | Zbl

[11] A. Cohen, R. DeVore, Ch. Schwab, “Analytic regularity and polynomial approximation of parametric and stochastic PDE's”, Anal. Appl. (Singap.), 9:1 (2011), 11–47 | DOI | MR | Zbl

[12] Dinh Dũng, Linear collective collocation and Galerkin approximations for parametric and stochastic elliptic PDEs, arXiv: 1511.03377

[13] Dinh Dũng, M. Griebel, “Hyperbolic cross approximation in infinite dimensions”, J. Complexity, 33 (2016), 55–88 | DOI | MR | Zbl

[14] Dinh Dũng, M. Griebel, Vu Nhat Huy, C. Rieger, “$\varepsilon$-dimension in infinite dimensional hyperbolic cross approximation and application to parametric elliptic PDEs”, J. Complexity, 46 (2018), 66–89 | DOI | MR | Zbl

[15] H. C. Elman, C. W. Miller, E. T. Phipps, R. S. Tuminaro, “Assessment of collocation and Galerkin approaches to linear diffusion equations with random data”, Int. J. Uncertain. Quantif., 1:1 (2011), 19–33 | DOI | MR | Zbl

[16] M. D. Gunzburger, C. G. Webster, Guannan Zang, “Stochastic finite element methods for partial differential equations with random input data”, Acta Numer., 23 (2014), 521–650 | DOI | MR | Zbl

[17] P. D. Lax, A. N. Milgram, “Parabolic equations”, Contributions to the theory of partial differential equations, Ann. of Math. Stud., 33, Princeton Univ. Press, Princeton, N.J., 1954, 167–190 | MR | Zbl

[18] G. Migliorati, F. Nobile, E. von Schwerin, R. Tempone, “Analysis of the discrete $L^2$ projection on polynomial spaces with random evaluations”, Found. Comput. Math., 14:3 (2014), 419–456 ; MOX rep. 46/2011, Politechnico di Milano, Milano, 2011, 38 pp. https://mox.polimi.it/publication-results/?id=322&tipo=add_qmox | DOI | MR | Zbl

[19] F. Nobile, R. Tempone, C. G. Webster, “A sparse grid stochastic collocation method for partial differential equations with random input data”, SIAM J. Numer. Anal., 46:5 (2008), 2309–2345 | DOI | MR | Zbl

[20] F. Nobile, R. Tempone, C. G. Webster, “An anisotropic sparse grid stochastic collocation method for partial differential equations with random input data”, SIAM J. Numer. Anal., 46:5 (2008), 2411–2442 | DOI | MR | Zbl

[21] Ch. Schwab, C. G. Gittelson, “Sparse tensor discretizations of high-dimensional parametric and stochastic PDEs”, Acta Numer., 20 (2011), 291–467 | DOI | MR | Zbl

[22] J. Zech, Dinh Dũng, Ch. Schwab, Multilevel approximation of parametric and stochastic PDEs, Res. rep. No. 2018-05, Seminar for Appied Mathematics, ETH, Zürich, 2018, 56 pp. https://www.math.ethz.ch/sam/research/reports.html