most citedRegularized infill criteria for multi-objective Bayesian optimization with application to aircraft design

11 citations · 23 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2025

NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis

Mouadh Yagoubi, David Danan, Milad Leyli-Abadi +15

The integration of machine learning (ML) into the physical sciences is reshaping computational paradigms, offering the potential to accelerate demanding simulations such as computa…

physics.flu-dyn2025

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations

Giovanni Catalani, Jean Fesquet, Xavier Bertrand +3

This paper introduces a novel surrogate modeling framework for aerodynamic applications based on Neural Fields. The proposed approach, MARIO (Modulated Aerodynamic Resolution Invar…

cs.LG20253 cited

Multi-objective Bayesian Optimization With Mixed-categorical Design Variables for Expensive-to-evaluate Aeronautical Applications

Nathalie Bartoli, Thierry Lefebvre, Rémi Lafage +8

This work aims at developing new methodologies to optimize computational costly complex systems (e.g., aeronautical engineering systems). The proposed surrogate-based method (often…

stat.ME20259 cited

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design

Paul Saves, Nathalie Bartoli, Youssef Diouane +5

Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context…

cs.LG202511 cited

Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design

Robin Grapin, Youssef Diouane, Joseph Morlier +4

Bayesian optimization is an advanced tool to perform ecient global optimization It consists on enriching iteratively surrogate Kriging models of the objective and the constraints b…

cs.LG2025

SMT-EX: An Explainable Surrogate Modeling Toolbox for Mixed-Variables Design Exploration

Mohammad Daffa Robani, Paul Saves, Pramudita Satria Palar +2

Surrogate models are of high interest for many engineering applications, serving as cheap-to-evaluate time-efficient approximations of black-box functions to help engineers and pra…