Global sensitivity analysis (SA) aims at quantifying the respective effects of input random
variables (or combinations thereof) onto the variance of the response of a physical or …

A powerful tool in computational stochastic mechanics is the stochastic finite element
method (SFEM). SFEM is an extension of the classical deterministic FE approach to the …

Discrepancies frequently occur between a physical system's responses and predictions
obtained from mathematical models. The Spectral Stochastic Finite Element Method …

Stationary systems modelled by elliptic partial differential equations—linear as well as
nonlinear—with stochastic coefficients (random fields) are considered. The mathematical …

The quantification of probabilistic uncertainties in the outputs of physical, biological, and
social systems governed by partial differential equations with random inputs require, in …

Mathematical models are widely used in many science disciplines, such as physics, biology
and meteorology. They are aimed at better understanding and explaining real-world …

Much attention has recently been devoted to the development of Stochastic Galerkin (SG)
and Stochastic Collocation (SC) methods for uncertainty quantification. An open and …

We propose a new robust technique for solving a class of linear stochastic partial differential
equations. The solution is approximated by a series of terms, each of which being the …

A stochastic response surface method (SRSM) which has been previously proposed for
problems dealing only with random variables is extended in this paper for problems in which …

Uncertainty quantification appears today as a crucial point in numerous branches of science
and engineering. In the last two decades, a growing interest has been devoted to a new …