36 citations · 56 across the 13 of their papers we have counts for
14 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…
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…
Global Sensitivity Analysis for Engineering Design Based on Individual Conditional Expectations
Pramudita Satria Palar, Paul Saves, Rommel G. Regis +4
Explainable machine learning techniques have gained increasing attention in engineering applications, especially in aerospace design and analysis, where understanding how input var…
Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration
Paul Saves, Pramudita Satria Palar, Muhammad Daffa Robani +6
Complex systems are increasingly explored through simulation-driven engineering workflows that combine physics-based and empirical models with optimization and analytics. Despite t…
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…
Frequency-aware Surrogate Modeling With SMT Kernels For Advanced Data Forecasting
Nicolas Gonel, Paul Saves, Joseph Morlier
This paper introduces a comprehensive open-source framework for developing correlation kernels, with a particular focus on user-defined and composition of kernels for surrogate mod…
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…
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…
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…
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…
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…
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations
Giovanni Catalani, Michael Bauerheim, Frédéric Tost +2
Advances in neural operators have introduced discretization invariant surrogate models for PDEs on general geometries, yet many approaches struggle to encode local geometric struct…
Bayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction
James M. Shihua, Paul Saves, Rhea P. Liem +1
Ensuring high accuracy and efficiency of predictive models is paramount in the aerospace industry, particularly in the context of multidisciplinary design and optimization processe…
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…