Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …

Surrogate models are popular tool to approximate the functional relationship of expensive
simulation models in multiple scientific and engineering disciplines. Successful use of …

Computer simulation has become the standard tool in many engineering fields for designing
and optimizing systems, as well as for assessing their reliability. Optimization and …

Global sensitivity analysis is now established as a powerful approach for determining the
key random input parameters that drive the uncertainty of model output predictions. Yet the …

Identifiability is a fundamental concept in parameter estimation, and therefore key to the
large majority of environmental modeling applications. Parameter identifiability analysis …

In this paper, a framework for uncertainty quantification (UQ) and global sensitivity analysis
(GSA) of composite wind turbine blades is presented. Because of the presence of …

Abstract Systems subject to uncertain inputs produce uncertain responses. Uncertainty
quantification (UQ) deals with the estimation of statistics of the system response, given a …

A review of theoretical concepts and diverse applications of sparse representations and
compressive sampling (CS) approaches in engineering mechanics problems is provided …

Polynomial chaos expansions (PCE) are well-suited to quantifying uncertainty in models
parameterized by independent random variables. The assumption of independence leads to …

In the field of uncertainty quantification, sparse polynomial chaos (PC) expansions are
commonly used by researchers for a variety of purposes, such as surrogate modeling. Ideas …