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International Journal for Uncertainty Quantification
インパクトファクター: 3.259 5年インパクトファクター: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN 印刷: 2152-5080
ISSN オンライン: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2016016805
pages 429-443

A MULTIMODES MONTE CARLO FINITE ELEMENT METHOD FOR ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS WITH RANDOM COEFFICIENTS

Xiaobing Feng
Department of Mathematics, The University of Tennessee, Knoxville, Tennessee 37996, USA
Junshan Lin
Department of Mathematics and Statistics, Auburn University, Auburn, Alabama 36849, USA
Cody Lorton
Department of Mathematics and Statistics, University of West Florida, Pensacola, Floridas 32514, USA

要約

This paper develops and analyzes an efficient numerical method for solving elliptic partial differential equations, where the diffusion coefficients are random perturbations of deterministic diffusion coefficients. The method is based upon a multimodes representation of the solution as a power series of the perturbation parameter, and the Monte Carlo technique for sampling the probability space. One key feature of the proposed method is that the governing equations for all the expanded mode functions share the same deterministic diffusion coefficient; thus an efficient direct solver by repeatedly using the LU decomposition of the discretized common deterministic diffusion operator can be employed for solving the finite element discretized linear systems. It is shown that the computational complexity of the algorithm is comparable to that of solving a few deterministic elliptic partial differential equations using the director solver. Error estimates are derived for the method, and numerical experiments are provided to test the efficiency of the algorithm and validate the theoretical results.


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