Uncertainty analysis in building energy assessment has become an active research field
because a number of factors influencing energy use in buildings are inherently uncertain …

This paper presents a review of the current state-of-the-art of numerical methods for
stochastic computations. The focus is on efficient high-order methods suitable for practical …

The@ first graduate-level textbook to focus on fundamental aspects of numerical methods
for stochastic computations, this book describes the class of numerical methods based on …

We discuss the arbitrary polynomial chaos (aPC), which has been subject of research in a
few recent theoretical papers. Like all polynomial chaos expansion techniques, aPC …

In recent years, there has been a growing interest in analyzing and quantifying the effects of
random inputs in the solution of ordinary/partial differential equations. To this end, the …

Uncertainty quantification (UQ) tasks, such as model calibration, uncertainty propagation,
and optimization under uncertainty, typically require several thousand evaluations of the …

This study reports on two strategies for accelerating posterior inference of a highly
parameterized and CPU‐demanding groundwater flow model. Our method builds on …

Parametrized families of PDEs arise in various contexts such as inverse problems, control
and optimization, risk assessment, and uncertainty quantification. In most of these …

A wide variety of uncertainty propagation methods exist in literature; however, there is a lack
of good understanding of their relative merits. In this paper, a comparative study on the …

Probabilistic analysis is increasing in popularity and importance within engineering and the
applied sciences. However, the stochastic perturbation technique is a fairly recent …