We provide an overview of the methods that can be used for prediction under uncertainty
and data fitting of dynamical systems, and of the fundamental challenges that arise in this …

We give a convergence proof for the approximation by sparse collocation of Hilbert-space-
valued functions depending on countably many Gaussian random variables. Such functions …

Groundwater flow model accuracy is often limited by the uncertainty in model parameters
that characterize aquifer properties and aquifer recharge. Aquifer properties such as …

This article introduces an hp hp‐adaptive multi‐element stochastic collocation method,
which additionally allows to re‐use existing model evaluations during either hh‐or pp …

Our study is keyed to the development of a viable framework for the stochastic
characterization of coreflooding simulation models under two-and three-phase flow …

We consider the problem of quantifying uncertainty regarding the output of an
electromagnetic field problem, in the presence of a large number of uncertain input …

In this work we propose an Uncertainty Quantification methodology for sedimentary basins
evolution under mechanical and geochemical compaction processes, which we model as a …

In this work an integrated methodological and operational framework for diagnosis and
calibration of Stratigraphic Forward Models (SFMs) which are typically employed for the …

We consider modeling approaches to characterize solute transport in porous media,
integrating them into a unique theoretical and experimental framework for model evaluation …

Key Points A time domain random walk approach is used to model breakthrough curves of
partitioning tracers observed in laboratory column experiments Sample distributions of …