Recent advances in sensor technologies, field methodologies, numerical modeling, and
inversion approaches have contributed to unprecedented imaging of hydrogeological …

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 …

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 …

Science and Engineering applications are typically associated with expensive optimization
problem to identify optimal design solutions and states of the system of interest. Bayesian …

Quantifying uncertainty associated with the microstructure variation of a material can be a
computationally daunting task, especially when dealing with advanced constitutive models …

This paper presents a Bayesian multifidelity uncertainty quantification framework, called
MFNets, which can be used to overcome three of the major challenges that arise when data …

We present an adaptive algorithm for constructing surrogate models of multi‐disciplinary
systems composed of a set of coupled components. With this goal we introduce “coupling” …

In this work we provide a convergence analysis for the quasi-optimal version of the sparse-
grids stochastic collocation method we presented in a previous work:“On the optimal …

We analyse convergence rates of Smolyak integration for parametric maps u: U→ X taking
values in a Banach space X, defined on the parameter domain U=[− 1, 1] N. For parametric …