Add to ORKGEngineering design optimization often gives rise to problems in which expensive objective functions are minimized by derivative-free methods. We propose a method for solving such problems that synthesizes ideas from the numerical optimization and computer experiment literatures. Our approach relies on kriging known function values to construct a sequence of surrogate models of the objective function that are used to guide a grid search for a minimizer. Results from numerical experiments on a standard test problem are presented. 16 page
To decrease the computational cost of genetic algorithm optimizations, surrogate models are used dur...
In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for ...
International audienceSurrogate models are often used to reduce the cost of design optimization prob...
Engineering design optimization often gives rise to problems in which expensive objective functions ...
Engineering design optimization often gives rise to problems in which expensive objective functions ...
The paper proposes a global optimization algorithm employing surrogate modeling and adaptive infill ...
Advances in optimization and numerical analysis methods as well as recent developments in Multidisci...
An efficient numerical approach for the design of optimal aerodynamic shapes is presented in this pa...
Aerodynamic design, like many other engineering applications, is increasingly relying on computation...
This paper describes the formulation of optimization techniques based on control theory for aerodyna...
The goal of the research reported here is to develop rigorous optimization algorithms to apply to so...
International audienceFor decades, numerical tool improvements enabled the optimization of complex p...
We present a method for robust optimization of nonsmooth objective functions. The optimization begin...
Modern engineering design optimization relies to a large extent on computer simulations of physical ...
The growth of computer power and storage capacity allowed engineers to tackle engineering design as ...
To decrease the computational cost of genetic algorithm optimizations, surrogate models are used dur...
In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for ...
International audienceSurrogate models are often used to reduce the cost of design optimization prob...
Engineering design optimization often gives rise to problems in which expensive objective functions ...
Engineering design optimization often gives rise to problems in which expensive objective functions ...
The paper proposes a global optimization algorithm employing surrogate modeling and adaptive infill ...
Advances in optimization and numerical analysis methods as well as recent developments in Multidisci...
An efficient numerical approach for the design of optimal aerodynamic shapes is presented in this pa...
Aerodynamic design, like many other engineering applications, is increasingly relying on computation...
This paper describes the formulation of optimization techniques based on control theory for aerodyna...
The goal of the research reported here is to develop rigorous optimization algorithms to apply to so...
International audienceFor decades, numerical tool improvements enabled the optimization of complex p...
We present a method for robust optimization of nonsmooth objective functions. The optimization begin...
Modern engineering design optimization relies to a large extent on computer simulations of physical ...
The growth of computer power and storage capacity allowed engineers to tackle engineering design as ...
To decrease the computational cost of genetic algorithm optimizations, surrogate models are used dur...
In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for ...
International audienceSurrogate models are often used to reduce the cost of design optimization prob...