A computationally efficient procedure for quantifying uncertainty and finding significant
parameters of uncertainty models is presented. To deal with the random nature of input …

This paper proposes an efficient method for estimating uncertainty propagation and
identifying influence factors contributing to uncertainty. In general, the system is dominated …

The quantification of the uncertainty effect of random system parameters, such as the
loading conditions, material and geometric properties, on the system output response has …

The application of a Point-Collocation Non-Intrusive Polynomial Chaos method to the
uncertainty quantification of a stochastic transonic aeroelastic wing problem has been …

In recent years, extensive research has been reported about a method which is called the
generalized polynomial chaos expansion. In contrast to the sampling methods, eg, Monte …

One of the important issues in uncertainty analysis is to find an effective way for propagating
uncertainty through the system. In this paper, the polynomial chaos expansion (PCE) was …

In this paper, the quantification of uncertainty effects on response variability in rotor systems
is investigated. To avoid the use of Monte Carlo simulation (MCS), one of the most …

Polynomial chaos expansions (PCE) are an attractive technique for uncertainty
quantification (UQ) due to their strong mathematical basis and ability to produce functional …

The accuracy and the computational efficiency of a Point-Collocation Non-Intrusive
Polynomial Chaos (NIPC) method applied to stochastic problems with multiple uncertain …

An efficient framework to obtain stochastic models of responses with polynomial chaos
expansion (PCE) using an adaptive least-squares approach is presented in this paper. PCE …