229 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université Fédérale de Toulouse Midi-PyrénéesFR19 papers
- Centre National de la Recherche ScientifiqueFR17 papers
- Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)FR15 papers
- Université Toulouse III - Paul SabatierFR8 papers
- École Nationale de l’Aviation CivileFR6 papers
- Office National d'Études et de Recherches AérospatialesFR5 papers
- Université Paris-SaclayFR5 papers
- Institut Clément AderFR4 papers
- Institut de Recherche en Astrophysique et PlanétologieFR4 papers
- Polytechnique MontréalCA4 papers
- Université de ToulouseFR4 papers
- Astrophysique, Instrumentation et ModélisationFR3 papers
4 papers · 1 filter
Aerodynamic Shape Design Space Exploration with Deep Latent Diffusion Model
Zhen Wei, Edouard Dufour, Colin Pelletier +2
We propose DiffGeo, a latent space diffusion-based generative framework for aerodynamic design space exploration under extreme data scarcity. DiffGeo combines a learned latent spac…
Efficient Multi-Objective Constrained Bayesian Optimization of Bridge Girder
Heine Havneraas Røstum, Joseph Morlier, Sebastien Gros +1
The buildings and construction sector is a significant source of greenhouse gas emissions, with cement production alone contributing 7~\% of global emissions and the industry as a…
Numerical optimization of aviation decarbonization scenarios: balancing traffic and emissions with maturing energy carriers and aircraft technology
Ian Costa-Alves, Nicolas Gourdain, François Gallard +3
Despite being considered a hard-to-abate sector, aviation's emissions will play an important role in long-term climate mitigation of transportation. The introduction of low-carbon…
Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations
Giovanni Catalani, Siddhant Agarwal, Xavier Bertrand +3
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The propo…