Multilevel quasi-Monte Carlo for random elliptic eigenvalue problems I: Regularity and error analysis

TL;DR

This paper develops and rigorously analyzes a multilevel quasi-Monte Carlo method for efficiently estimating the expected minimal eigenvalue in stochastic elliptic eigenvalue problems with high-dimensional random parameters.

Contribution

It introduces a multilevel QMC algorithm with error bounds for stochastic elliptic eigenvalue problems, extending to higher-order rules under smoothness assumptions.

Findings

Provides error bounds for the MLQMC method.

Extends analysis to higher-order QMC rules with smoothness.

Supports efficient uncertainty quantification in physical applications.

Abstract

Stochastic PDE eigenvalue problems are useful models for quantifying the uncertainty in several applications from the physical sciences and engineering, e.g., structural vibration analysis, the criticality of a nuclear reactor or photonic crystal structures. In this paper we present a multilevel quasi-Monte Carlo (MLQMC) method for approximating the expectation of the minimal eigenvalue of an elliptic eigenvalue problem with coefficients that are given as a series expansion of countably-many stochastic parameters. The MLQMC algorithm is based on a hierarchy of discretisations of the spatial domain and truncations of the dimension of the stochastic parameter domain. To approximate the expectations, randomly shifted lattice rules are employed. This paper is primarily dedicated to giving a rigorous analysis of the error of this algorithm. A key step in the error analysis requires bounds on the mixed derivatives of the eigenfunction with respect to both the stochastic and spatial variables simultaneously. Under stronger smoothness assumptions on the parametric dependence, our analysis also extends to multilevel higher-order quasi-Monte Carlo rules. An accompanying paper [Gilbert and Scheichl, 2022], focusses on practical extensions of the MLQMC algorithm to improve efficiency, and presents numerical results.