This paper is an attempt to bridge the conceptual gaps between researchers working on the
two widely used approaches based on positive definite kernels: Bayesian learning or …

In this article, we present a detailed overview of the literature on the design of computer
experiments. We classify the existing literature broadly into two categories, viz. static and …

This text provides a framework in which the main objectives of the field of uncertainty
quantification (UQ) are defined and an overview of the range of mathematical methods by …

While Pareto-based multiobjective optimization algorithms continue to show effectiveness
for a wide range of practical problems that involve mostly two or three objectives, their …

This paper is a contemporary review of QMC ('quasi-Monte Carlo') methods, that is, equal-
weight rules for the approximate evaluation of high-dimensional integrals over the unit cube …

In many fields, such as environmental risk assessment, agronomic system behavior,
aerospace engineering, and nuclear safety, mathematical models turned into computer code …

Nur wenige Methoden haben eine so langfristige Bedeutung für das Arbeitsleben eines
Ingenieurs wie die statistische Versuchsplanung. CAD-Programme ändern sich schnell, so …

Experiments have long been used to study the relationship between a set of inputs to a
physical system and the resulting output. Termed physical experiments in this text, there is a …

Abstract Space-filling properties are important in designing computer experiments. The
traditional maximin and minimax distance designs consider only space-filling in the full …

Monte Carlo is one of the most versatile and widely used numerical methods. Its
convergence rate, O (N− 1/2), is independent of dimension, which shows Monte Carlo to be …