Improved Efficiency of a Multi-Index FEM for Computational Uncertainty Quantification

TL;DR

This paper introduces a multi-index finite element method that enhances computational efficiency for uncertainty quantification in elliptic PDEs with random inputs, outperforming traditional multi-level approaches.

Contribution

It develops a novel multi-index algorithm combining spatial discretization, dimension truncation, and Monte Carlo sampling, with rigorous error and cost analysis.

Findings

Improved complexity over multi-level Monte Carlo FEM.

Applicable to Lipschitz domains and general mesh hierarchies.

Mathematically substantiates the efficiency of multi-index algorithms.

Abstract

We propose a multi-index algorithm for the Monte Carlo (MC) discretization of a linear, elliptic PDE with affine-parametric input. We prove an error vs. work analysis which allows a multi-level finite-element approximation in the physical domain, and apply the multi-index analysis with isotropic, unstructured mesh refinement in the physical domain for the solution of the forward problem, for the approximation of the random field, and for the Monte-Carlo quadrature error. Our approach allows Lipschitz domains and mesh hierarchies more general than tensor grids. The improvement in complexity over multi-level MC FEM is obtained from combining spacial discretization, dimension truncation and MC sampling in a multi-index fashion. Our analysis improves cost estimates compared to multi-level algorithms for similar problems and mathematically underpins the superior practical performance of multi-index algorithms for partial differential equations with random coefficients.