Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling.
The tremendous potential benefits of SA are, however, yet to be fully realized, both for …

Structural systems are consistently encountering the variabilities in material properties,
undesirable defects and loading environments, which may potentially shorten their designed …

This paper presents a novel distribution-based global sensitivity analysis based on the
Kullback–Leibler divergence derived directly from generalized polynomial chaos expansion …

This paper presents a novel adaptive sequential sampling method for building Polynomial
Chaos Expansion surrogate models. The technique enables one-by-one extension of an …

In this work we introduce a manifold learning-based method for uncertainty quantification
(UQ) in systems describing complex spatiotemporal processes. Our first objective is to …

In this paper, we present an adaptive algorithm to construct response surface
approximations of high‐fidelity models using a hierarchy of lower fidelity models. Our …

Modeling the impact of space weather events such as coronal mass ejections (CMEs) is
crucial to protecting critical infrastructure. The Space Weather Modeling Framework is a …

PyApprox is a Python-based one-stop-shop for probabilistic analysis of numerical models
such as those used in the earth, environmental and engineering sciences. Easy to use and …

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to
significantly reduce the variance of statistical estimators while preserving the bias of the …

The multi-fidelity metamodeling method can dramatically improve the efficiency of
metamodeling for computationally expensive engineering problems when multiple levels of …