In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …

Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

A simple nonrecursive form of the tensor decomposition in d dimensions is presented. It
does not inherently suffer from the curse of dimensionality, it has asymptotically the same …

This book results from a course developed by the author and reflects both his own and
collaborative research regarding the development and implementation of uncertainty …

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 …

Monte Carlo methods are a very general and useful approach for the estimation of
expectations arising from stochastic simulation. However, they can be computationally …

Polynomial chaos (PC) expansions are used in stochastic finite element analysis to
represent the random model response by a set of coefficients in a suitable (so-called …

Polynomial chaos (PC) expansions are used in stochastic finite element analysis to
represent the random model response by a set of coefficients in a suitable (so-called …

Mathematical and numerical modelling of the cardiovascular system is a research topic that
has attracted remarkable interest from the mathematical community because of its intrinsic …