A Polynomial Chaos Expansion in Dependent Random Variables

TL;DR

This paper develops a new polynomial chaos expansion for dependent random variables that does not rely on tensor-product structures, enabling more flexible uncertainty quantification in complex stochastic systems.

Contribution

It introduces a measure-consistent multivariate orthonormal polynomial basis for dependent variables, expanding the applicability of PCE without tensor-product assumptions.

Findings

Proves mean-square convergence of the generalized PCE.

Provides analytical formulas for mean and variance of outputs.

Demonstrates effectiveness through a stochastic boundary-value problem example.

Abstract

This paper introduces a new generalized polynomial chaos expansion (PCE) comprising measure-consistent multivariate orthonormal polynomials in dependent random variables. Unlike existing PCEs, whether classical or generalized, no tensor-product structure is assumed or required. Important mathematical properties of the generalized PCE are studied by constructing orthogonal decomposition of polynomial spaces, explaining completeness of orthogonal polynomials for prescribed assumptions, exploiting whitening transformation for generating orthonormal polynomial bases, and demonstrating mean-square convergence to the correct limit. Analytical formulae are proposed to calculate the mean and variance of a truncated generalized PCE for a general output variable in terms of the expansion coefficients. An example derived from a stochastic boundary-value problem illustrates the generalized PCE approximation in estimating the statistical properties of an output variable for 12 distinct non-product-type probability measures of input variables.