QMC integration for lognormal-parametric, elliptic PDEs: local supports and product weights

  • Lukas Herrmann
  • Christoph Schwab


We analyze convergence rates of quasi-Monte Carlo (QMC) quadratures for countably-parametric solutions of linear, elliptic partial differential equations (PDE) in divergence form with log-Gaussian diffusion coefficient, based on the error bounds in Nichols and Kuo (J Complex 30(4):444–468, 2014. We prove, for representations of the Gaussian random field PDE input with locally supported basis functions, and for continuous, piecewise polynomial finite element discretizations in the physical domain novel QMC error bounds in weighted spaces with product weights that exploit localization of supports of the basis elements representing the input Gaussian random field. In this case, the cost of the fast component-by-component algorithm for constructing the QMC points scales linearly in terms of the integration dimension. The QMC convergence rate \(\mathcal {O}(N^{-1+\delta })\) (independent of the parameter space dimension s) is achieved under weak summability conditions on the expansion coefficients.

Mathematics Subject Classification

65D30 65N30 60G60 



The authors acknowledge the help of Magdalena Keller, a M.Sc. student in the ETH Applied Maths program, for permission to use her C\(++\) implementation of the fast CBC algorithm.


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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Seminar for Applied MathematicsETH ZürichZurichSwitzerland

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