Stochastic analysis of random heterogeneous media provides useful information only if
realistic input models of the material property variations are used. These input models are …

The consideration of uncertainties is of particular importance for nuclear reactor safety,
where high safety standards for example ensure the integrity of the containment. By means …

We outline a methodology for forward uncertainty quantification in systems with uncertain
parameters, discontinuous model response, and a limited number of model runs. Our …

The paper aims to solve a robust optimization problem (optimization in presence of
uncertainty) for finding the optimal locations of a number of CO2 injection wells for …

Surrogate model development is a critical step for uncertainty quantification or other sample-
intensive tasks for complex computational models. In this work we develop a multi-output …

The objective of the present work is to establish a framework to design simple Arrhenius
mechanisms for simulation of diesel engine combustion. The goal is to predict auto-ignition …

We develop a preconditioned Bayesian regression method that enables sparse polynomial
chaos representations of noisy outputs for stochastic chemical systems with uncertain …

Subsurface flow and transport problems usually involve some degree of uncertainty.
Polynomial chaos expansion can be used as surrogate of physical models for uncertainty …

Research in powered hypersonic flight has thrived in the past decades with strong interests
from both military and civilian aerospace applications [1, 2]. Among others, one significant …

Consider a mathematical model with a finite number of random parameters. Variance based
sensitivity analysis provides a framework to characterize the contribution of the individual …