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 …

Global sensitivity analysis (GSA), particularly for Sobol index, is a powerful tool to quantify
the variation of model response sourced from the uncertainty of input variables over the …

The application of neural networks (NN) in groundwater (GW) level prediction has been
shown promising by previous works. Yet, previous works have relied on a variety of inputs …

Polynomial chaos expansion has received considerable attention in uncertainty
quantification since its great modeling capability for complex systems. However, considering …

Surrogate models are indispensable tools in uncertainty quantification and global sensitivity
analysis. Polynomial chaos expansion (PCE) is one of the most widely used surrogate …

In this paper, we discuss the sensitivity analysis of model response when the uncertain
model inputs are not independent of one other. In this case, two different kinds of sensitivity …

This paper presents an extended polynomial chaos formalism for epistemic uncertainties
and a new framework for evaluating sensitivities and variations of output probability density …

We consider a high-dimensional nonlinear computational model of a dynamical system,
parameterized by a vector-valued control parameter, in the presence of uncertainties …

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 …