Titre :

Multi-objective optimization using Deep Gaussian Processes: Application to Aerospace Vehicle Design

Auteur(s) :

Hebbal, Ali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau]
Brevault, Loïc [Auteur]
DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau]
Balesdent, Mathieu [Auteur]
DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau]
Talbi, El-Ghazali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Melab, Nouredine [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la manifestation scientifique :

AIAA Scitech 2019 Forum, 2019

Ville :

SAN DIEGO

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2019-01-07

Titre de l’ouvrage :

AIAA Scitech 2019 Forum, 2019

Mot(s)-clé(s) en anglais :

AEROSPACE VEHICLES
AVIATION
CONSTRAINED OPTIMIZATION
COSTS
FUNCTION EVALUATION
GAUSSIAN DISTRIBUTION
GAUSSIAN NOISE (ELECTRONIC)
VEHICLES

Discipline(s) HAL :

Sciences de l'ingénieur [physics]
Physique [physics]
Mathématiques [math]
Informatique [cs]

Résumé en anglais : [en]

This paper is focused on the problem of constrained multi-objective design optimization of aerospace vehicles. The design of such vehicles often involves disciplinary legacy models considered as black-box and computationally ...
Lire la suite >This paper is focused on the problem of constrained multi-objective design optimization of aerospace vehicles. The design of such vehicles often involves disciplinary legacy models considered as black-box and computationally expensive simulations characterized by a possible non-stationary behavior (an abrupt change in the response or a different smoothness along the design space). The expensive cost of an exact function evaluation makes the use of classical evolutionary multi-objective algorithms not tractable. While Bayesian Optimization based on Gaussian Process regression can handle the expensive cost of the evaluations, the non-stationary behavior of the functions can make it inefficient. A recent approach consisting of coupling Bayesian Optimization with Deep Gaussian Processes showed promising results for single-objective non-stationary problems. This paper presents an extension of this approach to the multi-objective context. The efficiency of the proposed approach is assessed with respect to classical optimization methods on an analytical test-case and on an aerospace design problem.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL