Stochastic Spectral Methods in Uncertainty Quantification

Abstract

Uncertainty quantification is an important part of a probabilistic design of structures. Nonetheless, common Monte Carlo methods are highly computationally demanding or even not feasible for this task, especially in case of mathematical models of physical problems solved by finite element method. Therefore, the paper is focused on the efficient alternative approach for uncertainty quantification-stochastic spectral expansion, represented herein by Polynomial Chaos Expansion. In recent years, an application of stochastic spectral methods in uncertainty quantification is the topic of research for many scientists in various fields of science and its efficiency was shown by various studies. The paper presents basic theoretical background of polynomial chaos expansion and its connection to uncertainty quantification. The possibility of efficient statistical and sensitivity analysis is investigated and an application in analytical examples with known reference solution is presented herein. Moreover, practical implementation of methodology is discussed and developed SW tool is presented herein.