Journal
SIAM REVIEW
Volume 61, Issue 3, Pages 509-545Publisher
SIAM PUBLICATIONS
DOI: 10.1137/19M126966X
Keywords
elliptic PDEs with random coefficients; log-normal coefficients; finite element analysis; Bayesian approach; Metropolis-Hastings algorithm; multilevel Monte Carlo
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Funding
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07A27344, LLNL-JRNL-630212]
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In this paper we address the problem of the prohibitively large computational cost of existing Markov chain Monte Carlo methods for large-scale applications with high-dimensional parameter spaces, e.g., in uncertainty quantification in porous media flow. We propose a new multilevel Metropolis-Hastings algorithm and give an abstract, problem-dependent theorem on the cost of the new multilevel estimator based on a set of simple, verifiable assumptions. For a typical model problem in subsurface flow, we then provide a detailed analysis of these assumptions and show significant gains over the standard Metropolis-Hastings estimator. Numerical experiments confirm the analysis and demonstrate the effectiveness of the method with consistent reductions of more than an order of magnitude in the cost of the multilevel estimator over the standard Metropolis-Hastings algorithm for tolerances epsilon < 10(-2).
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