125 citations · 195 across the 12 of their papers we have counts for
6 papers · 1 filter
Energy-aware frugal Bayesian optimization
Gaston Plat, Paul Saves, Nathalie Bartoli +2
Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled…
Hierarchical Bayesian optimization of an aircraft-based multi-agent system-of-systems
Paul Saves, Thierry Lefebvre, Nathalie Bartoli +4
Developing innovative system architectures increasingly relies on advanced modeling and optimization techniques to frame the architecting process and define the corresponding compu…
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…
Surrogate-based optimization of system architectures subject to hidden constraints
Jasper Bussemaker, Paul Saves, Nathalie Bartoli +2
The exploration of novel architectures requires physics-based simulation due to a lack of prior experience to start from, which introduces two specific challenges for optimization…
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…
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes
Paul Saves, Remi Lafage, Nathalie Bartoli +6
The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. Thi…