This dissertation takes place in the framework of design and analysis of computer experiments. More precisely, its main focus is on optimization strategies based on surrogate models of the objective function, or metamodels. Its principal motivation is to expose and strengthen existing works on Kriging-based optimization. Some relationships between different classical metamodels are adressed, and some light is shed on the versatility of Kriging and its suitability for sequential and parallel optimization. After a detailed introduction to Kriging (end of part I), several tracks for the enrichment of this metamodel are proposed in part II. Part III is dedicated to some novelties in Krigingbased optimization, in particular concerning the integr...
The use of kriging metamodels in simulation optimization has become increasingly popular during rece...
This survey considers the optimization of simulated systems. The simulation may be either determinis...
This paper demonstrates the application of correlated Gaussian process based approximations to optim...
Cette thèse s'inscrit dans la thématique de planification d'expériences numériques. Elle porte sur l...
This papers aims at presenting a review of some metamodels used in optimization. We are interested i...
We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and th...
International audienceThe optimization of expensive-to-evaluate functions generally relies on metamo...
Meta-models proved to be a very efficient strategy for optimization of expensive black-box models, e...
Les premières phases de conception d'une turbomachine telle qu'un fan de refroidissement automobile,...
Les turbomachines aéronautiques sont composées de plusieurs roues aubagées dont la fonction estde tr...
Application of interpolation/approximation techniques (metamodels, for brevity) is commonly adopt...
This article surveys optimization of simulated systems. The simulation may be either deterministic o...
Kriging-based exploration strategies often rely on a single Ordinary Kriging model which parametric ...
Abstract: This article surveys optimization of simulated systems. The simulation may be either deter...
This paper describes an experiment exploring the potential of kriging metamodeling for multi-objecti...
The use of kriging metamodels in simulation optimization has become increasingly popular during rece...
This survey considers the optimization of simulated systems. The simulation may be either determinis...
This paper demonstrates the application of correlated Gaussian process based approximations to optim...
Cette thèse s'inscrit dans la thématique de planification d'expériences numériques. Elle porte sur l...
This papers aims at presenting a review of some metamodels used in optimization. We are interested i...
We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and th...
International audienceThe optimization of expensive-to-evaluate functions generally relies on metamo...
Meta-models proved to be a very efficient strategy for optimization of expensive black-box models, e...
Les premières phases de conception d'une turbomachine telle qu'un fan de refroidissement automobile,...
Les turbomachines aéronautiques sont composées de plusieurs roues aubagées dont la fonction estde tr...
Application of interpolation/approximation techniques (metamodels, for brevity) is commonly adopt...
This article surveys optimization of simulated systems. The simulation may be either deterministic o...
Kriging-based exploration strategies often rely on a single Ordinary Kriging model which parametric ...
Abstract: This article surveys optimization of simulated systems. The simulation may be either deter...
This paper describes an experiment exploring the potential of kriging metamodeling for multi-objecti...
The use of kriging metamodels in simulation optimization has become increasingly popular during rece...
This survey considers the optimization of simulated systems. The simulation may be either determinis...
This paper demonstrates the application of correlated Gaussian process based approximations to optim...