125 citations · 195 across the 12 of their papers we have counts for
12 papers
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…
Efficient multidisciplinary design via Bayesian optimization
Nathalie Bartoli, Thierry Lefebvre, Rémi Lafage +3
This study introduces SEGOMOE, a Bayesian optimization tool for optimizing complex, computationally expensive systems, especially in aeronautics. It efficiently handles mixed desig…
System-of-systems Modeling and Optimization: An Integrated Framework for Intermodal Mobility
Paul Saves, Jasper Bussemaker, Rémi Lafage +4
For developing innovative systems architectures, modeling and optimization techniques have been central to frame the architecting process and define the optimization and modeling p…
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…
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…
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…
System Architecture Optimization Strategies: Dealing with Expensive Hierarchical Problems
Jasper H. Bussemaker, Paul Saves, Nathalie Bartoli +2
Choosing the right system architecture for the problem at hand is challenging due to the large design space and high uncertainty in the early stage of the design process. Formulati…
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft
Paul Saves, Youssef Diouane, Nathalie Bartoli +2
Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be u…
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…
A mixed-categorical correlation kernel for Gaussian process
P. Saves, Y. Diouane, N. Bartoli +2
Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different…