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Multilevel Quasi-Monte Carlo Methods for Lognormal Diffusion Problems
by F. Kuo and R. Scheichl and Ch. Schwab and I. Sloan and E. Ullmann
(Report number 2015-22)
Abstract
In this paper we present a rigorous cost and error analysis of a
multilevel estimator based on randomly shifted
Quasi-Monte Carlo (QMC) lattice rules for lognormal diffusion problems.
These problems are motivated by uncertainty quantification problems in
subsurface flow.
We extend the convergence analysis in [Graham et al.,
Numer. Math. 2014]
to multilevel Quasi-Monte Carlo finite element
discretizations and give a constructive proof of the dimension-independent
convergence of the QMC rules. More precisely, we provide suitable
parameters for the construction of such rules that yield
the required variance reduction
for the multilevel scheme to achieve an ε-error with a cost of O(ε−θ) with θ<2, and in practice even θ≈1, for sufficiently fast decaying covariance kernels
of the underlying Gaussian random field inputs. This confirms that
the computational gains due to the application of multilevel sampling
methods and the gains due to the application of QMC methods,
both demonstrated in earlier works for the same model problem,
are complementary. A
series of numerical experiments confirms these gains.
The results show that in practice the
multilevel QMC method consistently outperforms both the multilevel MC method and
the single-level variants
even for non-smooth problems.
Keywords:
BibTeX@Techreport{KSSSU15_612, author = {F. Kuo and R. Scheichl and Ch. Schwab and I. Sloan and E. Ullmann}, title = {Multilevel Quasi-Monte Carlo Methods for Lognormal Diffusion Problems}, institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich}, number = {2015-22}, address = {Switzerland}, url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2015/2015-22.pdf }, year = {2015} }
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