Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …

We present two recently released R packages, DiceKriging and DiceOptim, for the
approximation and the optimization of expensive-to-evaluate deterministic functions …

Gaussian Process (GP) models are a class of flexible non-parametric models that have rich
representational power. By using a Gaussian process with additive structure, complex …

We develop a framework for multifidelity information fusion and predictive inference in high-
dimensional input spaces and in the presence of massive data sets. Hence, we tackle …

Gaussian Process models are often used for predicting and approximating expensive
experiments. However, the number of observations required for building such models may …

The method of derivative based global sensitivity measures (DGSM) has recently become
popular among practitioners. It has a strong link with the Morris screening method and …

In-stent restenosis is a recurrence of coronary artery narrowing due to vascular injury
caused by balloon dilation and stent placement. It may lead to the relapse of angina …

In the framework of the estimation of safety margins in nuclear accident analysis, a
quantitative assessment of the uncertainties tainting the results of computer simulations is …

The topic of (statistical) computer experiments is a relatively new field of research. Many
researchers consider the work of Sacks et al.(1989) to be the seminal paper on computer …

Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a
Gaussian Process regression (GPR) model to approximate the objective function and an …