The forty-fifth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, March 31, 2022.
This work aims at developing new methodologies to optimize computational ostly complex systems (e.g., aeronautical engineering systems). The proposed surrogate-based method (often
called Bayesian Optimization) uses adaptive sampling to promote a trade-off between exploration and exploitation. Our in-house implementation, called SEGOMOE, handles a high number of
design variables (continuous, discrete or categorical) and nonlinearities by combining mixtures of experts (local surrogate models) for the objective and/or the constraints. An extension to multi-fidelity is also included when a variety of information is available. The performance of the proposed approach has been evaluated on both a benchmark of analytical constrained and
unconstrained problems a well as a set of realistic aeronautical applications.
Organizing committee: Pierre Barbillon (MIA-Paris), Julien Bect (L2S), Nicolas Bousquet (EDF R&D), Amélie Fau (LMPS), Filippo Gatti (LMPS), Bertrand Iooss (EDF R&D), Alexandre Janon (LMO), Sidonie Lefebvre (ONERA), Didier Lucor (LISN), Emmanuel Vazquez (L2S).
Coordinator: Julien Bect (L2S).
Practical details: the seminar will be held online using Microsoft Teams.
If you want to attend this seminar (or any of the forthcoming online UQSay seminars), and if you do not already have access to the UQSay group on Teams, simply send an email and you will be invited. Please specify which email address the invitation must be sent to (this has to be the address associated with your Teams account).
You will find the link to the seminar on the “General” UQSay channel on Teams, approximately 15 minutes before the beginning.
The technical side of things: you can use Teams either directly from your web browser or using the “fat client”, which is available for most platforms (Windows, Linux, Mac, Android & iOS). We strongly recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.