Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

In the framework of risk assessment, computer codes are increasingly used to understand,
model and predict physical phenomena. As these codes can be very time-consuming to run …

This article reviews the design and analysis of simulation experiments. It focusses on
analysis via two types of metamodel (surrogate. emulator); namely, low-order polynomial …

This contribution summarizes the design and analysis of experiments with computerized
simulation models. It focuses on two metamodel (surrogate, emulator) types, namely first …

This article addresses the problem of derivative-free (single-or multi-objective) optimization
subject to multiple inequality constraints. Both the objective and constraint functions are …

A Bayesian reinforcement learning reliability method that combines Bayesian inference for
the failure probability estimation and reinforcement learning-guided sequential experimental …

This article surveys the most relevant kriging-based infill algorithms for multiobjective
simulation optimization. These algorithms perform a sequential search of so-called infill …

Optimization of expensive computer models with the help of Gaussian process emulators is
now commonplace. However, when several (competing) objectives are considered …

In many real world problems, we want to infer some property of an expensive black-box
function f, given a budget of T function evaluations. One example is budget constrained …

To alleviate the intensive computational burden of reliability analysis, a new parallel active
learning reliability method is proposed from the multi-point look-ahead paradigm. First, in the …