Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Statistics
  • Help & Advice
University of Warwick

The Library

  • Login

Uncertainty quantification and weak approximation of an elliptic inverse problem

Tools
- Tools
+ Tools

Dashti, M. and Stuart, A. M.. (2011) Uncertainty quantification and weak approximation of an elliptic inverse problem. SIAM Journal on Numerical Analysis, Vol.49 (No.6). pp. 2524-2542. ISSN 0036-1429

[img]
Preview
Text
WRAP_Stuart_SNA002524.pdf - Draft Version

Download (312Kb) | Preview
Official URL: http://dx.doi.org/10.1137/100814664

Abstract

We consider the inverse problem of determining the permeability from the pressure in a Darcy model of flow in a porous medium. Mathematically the problem is to find the diffusion coefficient for a linear uniformly elliptic partial differential equation in divergence form, in a bounded domain in dimension $d \le 3$, from measurements of the solution in the interior. We adopt a Bayesian approach to the problem. We place a prior random field measure on the log permeability, specified through the Karhunen–Loève expansion of its draws. We consider Gaussian measures constructed this way, and study the regularity of functions drawn from them. We also study the Lipschitz properties of the observation operator mapping the log permeability to the observations. Combining these regularity and continuity estimates, we show that the posterior measure is well defined on a suitable Banach space. Furthermore the posterior measure is shown to be Lipschitz with respect to the data in the Hellinger metric, giving rise to a form of well posedness of the inverse problem. Determining the posterior measure, given the data, solves the problem of uncertainty quantification for this inverse problem. In practice the posterior measure must be approximated in a finite dimensional space. We quantify the errors incurred by employing a truncated Karhunen–Loève expansion to represent this meausure. In particular we study weak convergence of a general class of locally Lipschitz functions of the log permeability, and apply this general theory to estimate errors in the posterior mean of the pressure and the pressure covariance, under refinement of the finite-dimensional Karhunen–Loève truncation.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Mathematics
Library of Congress Subject Headings (LCSH): Darcy's law, Inverse problems (Differential equations), Differential equations, Elliptic
Journal or Publication Title: SIAM Journal on Numerical Analysis
Publisher: Society for Industrial and Applied Mathematics
ISSN: 0036-1429
Date: 2011
Volume: Vol.49
Number: No.6
Page Range: pp. 2524-2542
Identification Number: 10.1137/100814664
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Engineering and Physical Sciences Research Council (EPSRC), European Research Council (ERC)
References: [1] R. A. Adams, Sobolev Spaces, Pure Appl. Math. 65, Academic Press, New York, London, 1975. [2] I. Babuˇska, F. Nobile, and R. Tempone, A stochastic collocation method for elliptic partial differential equations with random input data, SIAM J. Numer. Anal., 45 (2007), pp. 1005– 1034. [3] I. Babuˇska, R. Tempone, and G. E. Zouraris, Galerkin finite element approximations of stochastic elliptic partial differential equations, SIAM J. Numer. Anal., 42 (2004), pp. 800– 825. [4] M. Bieri and C. Schwab, Sparse high order FEM for elliptic sPDEs, Comput. Methods Appl. Mech. Engrg., 198 (2009), pp. 1149–1170. [5] V. I. Bogachev, Gaussian Measures, Math. Surveys Monogr. 62, American Mathematical Society, Providence, RI, 1998. [6] J. Charrier, Strong and weak error estimates for elliptic partial differential equations with random coefficients, submitted. [7] A. Cohen, R. DeVore, and C. Schwab, Analytic regularity and polynomial approximation of parametric and stochastic elliptic PDEs, Anal. Appl. (Singap.), 9 (2011), pp. 11–47. [8] S. L. Cotter, M. Dashti, J. C. Robinson, and A. M. Stuart, Bayesian inverse problems for functions with applications in fluid mechanics, Inverse Problems, 25 (2009), 115008. [9] S. L. Cotter, M. Dashti, and A. M. Stuart, Approximation of Bayesian inverse problems in differential equations, SIAM J. Numer. Anal., 48 (2010), pp. 322–345. [10] S. L. Cotter, M. Dashti, and A. M. Stuart, Variational data assimilation using targeted random walks, Int. J. Numer. Methods Fluids, to appear. [11] S. L. Cotter, G. O. Roberts, A. M. Stuart, and D. White, MCMC methods for functions: Modifying old algorithms to make them faster, submitted. [12] G. Da Prato and J. Zabczyk, Stochastic Equations in Infinite Dimensions, Encyclopedia Math. Appl. 44, Cambridge University Press, Cambridge, UK, 1992. [13] M. Dashti, S. Harris, and A. M. Stuart, Besov priors for Bayesian inverse problems, submitted. [14] M. Dashti, K. J. H. Law, A. M. Stuart, and J. Voss, MAP estimators for Bayesian nonparametrics, in preparation. [15] R. M. Dudley, Real Analysis and Probability, 2nd ed., Cambridge University Press, UK, 2002. [16] P. Frauenfelder, C. Schwab, and R. A. Todor, Finite elements for elliptic problems with stochastic coefficients, Comput. Methods Appl. Mech. Engrg., 194 (2005), pp. 205–228. [17] D. Gilbarg and N. S. Trudinger, Elliptic Partial Differential Equations of Second Order, Springer-Verlag, Berlin, 1983. [18] M. Lassas, E. Saksman, and S. Siltanen, Discretization-invariant Bayesian inversion and Besov space priors, Inverse Prob. Imaging, 3 (2009), pp. 87–122. [19] M. A. Lifshits, Gaussian Random Functions, Math. Appl. 322, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1995. [20] H. G. Matthies, Stochastic finite elements: Computational approaches to stochastic partial differential equations, ZAMM Z. Angew. Math. Mech., 88 (2008), pp. 849–873. [21] H. G. Matthies and C. Bucher, Finite elements for stochastic media problems, Comput. Methods Appl. Mech. Engrg., 168 (1999), pp. 3–17. [22] D. McLaughlin and L. Townley, A reassessment of the ground water inverse problem, Water Resour. Res., 32 (1996), pp. 1131–1161. [23] 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), pp. 2411–2442. [24] F. Nobile, R. Tempone, and C. G. Webster, A sparse grid stochastic collocation method for partial differential equations with random input data, SIAM J. Numer. Anal., 46 (2008), pp. 2309–2345. [25] C. Schwab and R. A. Todor, Karhunen-Lo`eve approximation of random fields by generalized fast multipole methods, J. Comput. Phys., 217 (2006), pp. 100–122. [26] S. V. Shaposhnikov, On Morrey’s estimate for the Sobolev norms of solutions of elliptic equations, (Russian) Mat. Zametki, 79 (2006), pp. 450–469; translation in Math. Notes, 79 (2006), pp. 413–430. [27] A. M. Stuart, Inverse problems: A Bayesian perspective, Acta Numer., 19 (2010), pp. 451–559. [28] A. Zygmund, Trigonometric Series. Vol. I, II. Reprint of the 1979 edition. Cambridge Math. Lib., Cambridge University Press, Cambridge, UK, 1988. [29] D. Zhang and Z. Lu, An efficient, high-order perturbation approach for flow in random porous media via Karhunen-Lo`eve and polynomial expansions, J. Comput. Phys., 194 (2004), pp. 773–794.
URI: http://wrap.warwick.ac.uk/id/eprint/44116

Request changes to a record

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...
twitter

Email us: publications@warwick.ac.uk
Contact Details
About Us