The forty-fifth UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, March 31, 2022.
2–3 PM — Nathalie Bartoli (ONERA) — [slides]
Bayesian optimization to solve mono- or multi-fidelity constrained black box problem
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.
- P. Saves, N. Bartoli, Y. Diouane, T. Lefebvre, J. Morlier, C. David, S. Defoort (2022). Multidisciplinary design optimization with mixed categorical variables for aircraft design. In AIAA SCITECH 2022 Forum (p. 0082).
- R. C. Arenzana, A. López-Lopera, S. Mouton, N. Bartoli, T. Lefebvre (2021, July). Multifidelity Gaussian Process model for CFD and Wind Tunnel data fusion. In Proceedings of the International Conference on Multidisciplinary Design Optimization of Aerospace Systems (AEROBEST 2021) (pp. 1-758).
- R. Priem, H. Gagnon, I. Chittick, S. Dufresne, Y. Diouane, and N. Bartoli (2020). An efficient application of Bayesian optimization to an industrial MDO framework for aircraft design. In AIAA AVIATION 2020 FORUM (p. 3152).
- R. Priem, N. Bartoli, Y. Diouane, A. Sgueglia (2020), Upper trust bound feasibility criterion for mixed constrained Bayesian optimization with application to aircraft design, Aerospace Science and Technology
- M. Meliani, N. Bartoli, T. Lefebvre, M.-A. Bouhlel, J. R. R. A. Martins, J. Morlier, Multi-fidelity efficient global optimization: Methodology and application to airfoil shape design, 20th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, June 2019, Dallas, United States
- N. Bartoli, T. Lefebvre, S. Dubreuil, R. Olivanti, R. Priem, N. Bons, J. R. R. A. Martins, J. Morlier (2019), Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design, Aerospace Science and Technology Journal, vol. 90, p. 85-102
- M.-A. Bouhlel, J. T. Hwang, N. Bartoli, R. Lafage, J. Morlier, J. R. R. A. Martins (2019), A Python surrogate modeling framework with derivatives, Advances in Engineering Software
- M.-A. Bouhlel, N. Bartoli, A. Otsmane and J. Morlier, Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction, Structural and Multidisciplinary Optimization, vol 53, no5, pp 935-952, 2016
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.