Offre en lien avec l’Action/le Réseau : RoCED/– — –

Laboratoire/Entreprise : Ecole Centrale Lyon
Durée : 12 months
Contact : celine.helbert@ec-lyon.fr
Date limite de publication : 2024-03-31

Contexte :
Including model and environmental uncertainties in decision aiding methods is often seen as becoming increasingly important. This is the case when seeking optimal renewing strategies for buildings.
However the theory and the algorithms for optimizing in the presence of uncertainties is still an active research domain, particularly when optimizing many criteria.
In this post-doctoral work, we will focus on costly and general nonlinear constrained multi-objective optimization problems that are affected by uncertainties. We will consider the case where the uncertain parameters can be separated from the optimization variables and can be chosen during the simulations. Because of this separation and providing a probability of occurence of the uncertainties exists, a statistical modeling in the joint design × uncertain parameters space is possible. This will be the context of the work.

Sujet :
The goal of this work is to improve the ideas introduced in [El Amri 23] by putting them in the context of multi-objective optimization under uncertainties. The expected hyper-volume improvement must be adapted to take into account the uncertainties and a sampling SUR criterion must be devised to choose the value of the random parameter to be evaluated. A multi-output Gaussian process can be proposed to take into account the correlation between the objective functions. A wise choice of the correlation kernel should be done.
The methods developed will be applied to the design of energy efficient
buildings, a major contemporary challenge. The criteria are the energy usage of the building, the thermal comfort and the cost. Important uncertainties affect the cost (through the cost of energy) and the external conditions through the climate change.
[El Amri] : R. El Amri, R. Le Riche, C. Helbert, C. Blanchet-Scalliet and S. Da Veiga, A sampling criterion for constrained Bayesian optimization with uncertainties, to appear in SMAI Journal of Computational Mathematics, 2023.

Profil du candidat :
• doctoral degree or equivalent in mathematics,
• proven strong background in uncertainty quantification or statistical learning theory,
• substantial experience in numerical programming.

Formation et compétences requises :
See above

Adresse d’emploi :
Institut Camille Jordan (ICJ), Campus of l’Ecole Centrale de Lyon, Ecully.
Stays will be expected at the LIMOS laboratory, either in Clermont-Ferrand, or in Saint-Etienne, FR.

Document attaché : 202312141710_postdocoffer_moo_uncertainties.pdf