Multilevel Quasi-Monte Carlo Methods for Lognormal Diffusion Problems

TL;DR

This paper rigorously analyzes multilevel Quasi-Monte Carlo methods for lognormal diffusion problems, demonstrating their efficiency and convergence properties in uncertainty quantification for subsurface flow.

Contribution

It extends convergence analysis of QMC methods to multilevel discretizations and provides practical guidelines for achieving optimal variance reduction.

Findings

Multilevel QMC achieves $ heta < 2$ cost for $ ext{error} o 0$

Numerical experiments confirm efficiency gains over MC methods

Multilevel QMC outperforms single-level variants even for non-smooth problems

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 $\varepsilon$-error with a cost of $\mathcal{O}(\varepsilon^{-\theta})$ with $\theta < 2$, and in practice even $\theta \approx 1$, 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.