This article reviews blackbox optimization applications of direct search optimization methods
over the past twenty years. Emphasis is placed on the Mesh Adaptive Direct Search (Mads) …

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

We report on an empirical study of the main strategies for quantile regression in the context
of stochastic computer experiments. To ensure adequate diversity, six metamodels are …

Global sensitivity analysis aims at quantifying the impact of input variability onto the variation
of the response of a computational model. It has been widely applied to deterministic …

Due to limited computational power, performing uncertainty quantification analyses with
complex computational models can be a challenging task. This is exacerbated in the context …

In the context of uncertainty quantification, computational models are required to be
repeatedly evaluated. This task is intractable for costly numerical models. Such a problem …

Stochastic simulators are ubiquitous in many fields of applied sciences and engineering. In
the context of uncertainty quantification and optimization, a large number of simulations is …

This paper investigates the construction of a metamodel for coastal flooding early warning at
the peninsula of Gâvres, France. The code under study is an hydrodynamic model which …

We propose a deep learning-based surrogate model for stochastic simulators. The basic
idea is to use a generative neural network to approximate the stochastic response. The …

Sensitivity indices are commonly used to quantify the relative influence of any specific group
of input variables on the output of a computer code. In this paper, we focus both on computer …