Generalized dimension truncation error analysis for high-dimensional numerical integration: lognormal setting and beyond

TL;DR

This paper develops a theoretical framework for analyzing the error caused by truncating the dimension of input random fields in high-dimensional PDE integrals, applicable to various discretizations and nonlinear quantities, with improved bounds for lognormal cases.

Contribution

It introduces a Taylor series-based method for deriving dimension truncation error bounds applicable to non-affine and nonlinear PDE models, including lognormal diffusion coefficients.

Findings

Derived dimension truncation rates for broad PDE classes.

Applicable to non-affine parametric operator equations.

Provided improved error bounds for lognormal PDEs.

Abstract

Partial differential equations (PDEs) with uncertain or random inputs have been considered in many studies of uncertainty quantification. In forward uncertainty quantification, one is interested in analyzing the stochastic response of the PDE subject to input uncertainty, which usually involves solving high-dimensional integrals of the PDE output over a sequence of stochastic variables. In practical computations, one typically needs to discretize the problem in several ways: approximating an infinite-dimensional input random field with a finite-dimensional random field, spatial discretization of the PDE using, e.g., finite elements, and approximating high-dimensional integrals using cubatures such as quasi-Monte Carlo methods. In this paper, we focus on the error resulting from dimension truncation of an input random field. We show how Taylor series can be used to derive theoretical dimension truncation rates for a wide class of problems and we provide a simple checklist of conditions that a parametric mathematical model needs to satisfy in order for our dimension truncation error bound to hold. Some of the novel features of our approach include that our results are applicable to non-affine parametric operator equations, dimensionally-truncated conforming finite element discretized solutions of parametric PDEs, and even compositions of PDE solutions with smooth nonlinear quantities of interest. As a specific application of our method, we derive an improved dimension truncation error bound for elliptic PDEs with lognormally parameterized diffusion coefficients. Numerical examples support our theoretical findings.