Journal of Applied Mathematics and Computing

, Volume 38, Issue 1–2, pp 367–387

Nonparametric density estimation for randomly perturbed elliptic problems III: convergence, computational cost, and generalizations

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Abstract

This is the third in a series of three papers on nonparametric density estimation for randomly perturbed elliptic problems. In the previous papers by Estep, Målqvist, and Tavener (SIAM J. Sci. Comput. 31:2935–2959, 2009; Int. J. Numer. Methods Eng. 80:846–867, 2009), we derive an a posteriori error estimate for a computed probability distribution and an efficient adaptive algorithm for propagation of uncertainty into a quantity of interest computed from numerical solutions of an elliptic partial differential equation. We also test the algorithm on various problems including an example relevant to oil reservoir simulation. In this paper, we derive a convergence result for the method based on the assumption that the underlying domain decomposition algorithm converges geometrically. The main ideas of the proof can be applied to a large class of domain decomposition algorithms. We also present several generalizations of the method and an analysis of the computational cost.

Keywords

A posteriori error analysis Domain decomposition Convergence Elliptic problem Neumann series Nonparametric density estimation Random perturbation Forward sensitivity analysis 

Mathematics Subject Classification (2000)

65N15 65N30 65N55 65C05 

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Copyright information

© Korean Society for Computational and Applied Mathematics 2011

Authors and Affiliations

  • Donald Estep
    • 1
  • Michael J. Holst
    • 2
  • Axel Målqvist
    • 3
  1. 1.Department of StatisticsColorado State UniversityFort CollinsUSA
  2. 2.Departments of Mathematics and PhysicsUC San DiegoLa JollaUSA
  3. 3.Department of Information TechnologyUppsala UniversityUppsalaSweden

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