270 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-PyrénéesFR53 papers
- Centre National de la Recherche ScientifiqueFR40 papers
- Office National d'Études et de Recherches AérospatialesFR34 papers
- Université de ToulouseFR33 papers
- Université Toulouse III - Paul SabatierFR26 papers
- École Nationale de l’Aviation CivileFR25 papers
- Institut de Recherche en Informatique de ToulouseFR16 papers
- National Higher French Institute of Aeronautics and Space15 papers
- Centre National d'Études SpatialesFR12 papers
- Institut de Recherche en Astrophysique et PlanétologieFR11 papers
- Institut Clément AderFR10 papers
- Institut Polytechnique de BordeauxFR10 papers
159 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…
A cosmic-ray loaded nascent outflow driven by a massive star cluster
Marianne Lemoine-Goumard, Lucia Härer, Lars Mohrmann +6
Cosmic rays are widely held to drive outflows from star-forming galaxies and profoundly influence galaxy evolution. Direct evidence for cosmic-ray carrying outflows is however lack…
Compressing ACAS-Xu Lookup Tables with Binary Decision Diagrams
Martin Boniol, Julien Brunel, Jean-Baptiste Chaudron +2
The Airborne Collision Avoidance System Xu (ACAS-Xu) relies on large certified Look-Up Tables (LUTs) that encode the exact decision logic used in operation. Neural-network-based ap…
Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
Pramudita Satria Palar, Paul Saves, Muhammad Daffa Robani +6
The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models play a central role in reducing…
Safe and wind-aware synchronous path planning for a fleet of fixed-wing constant speed aircraft
Maël Feurgard, Gautier Hattenberger, Nicolas Durand +1
Path planning for multiple unmanned aerial vehicles is a difficult task, and even more for a fleet of fixed-wing aircraft. One specific case is the transition to, or between, forma…
An Open Database of Lunar Regolith and Simulants Properties
Léonie Gasteiner, Naomi Murdoch, Olfa D'Angelo
Lunar regolith, the layer of unconsolidated material covering the Moon's surface, is central to the science and technology developed for the Moon, notably related to in-situ scienc…
Real-Time Evaluation of an Ultra-Tight GNSS/INS Integration Based on Adaptive PLL Bandwidth
Gaël Pages, Priot Benoît, Guillaume Beaugendre
In this contribution, we propose a GNSS/INS ultra-tight coupling in which the GNSS receiver architecture is based on a vector tracking loop type architecture. In the proposed appro…
Optimized Area Coverage in Disaster Response Utilizing Autonomous UAV Swarm Formations
Lampis Papakostas, Aristeidis Geladaris, Athanasios Mastrogeorgiou +4
This paper presents a UAV swarm system designed to assist first responders in disaster scenarios like wildfires. By distributing sensors across multiple agents, the system extends…
Robust H2/H-infinity control under stochastic requirements: minimizing conditional value-at-risk instead of worst-case performance
Ervan Kassarian, Francesco Sanfedino, Daniel Alazard +1
Conventional robust H2/H-infinity control minimizes the worst-case performance, often leading to a conservative design driven by very rare parametric configurations. To reduce this…
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems
Florian Courteville, Iain Henderson, Denis Matignon +1
In order to learn distributed port-Hamiltonian systems (dPHS) using Gaussian processes (GPs), the partitioned finite element method (PFEM) is combined with the Gp-dPHS method. By f…
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…
Synthetic Data for Robust Runway Detection
Estelle Chigot, Dennis G. Wilson, Meriem Ghrib +2
Deep vision models are now mature enough to be integrated in industrial and possibly critical applications such as autonomous navigation. Yet, data collection and labeling to train…
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…
Load Limiting Control for Component Life Extension
Chams Eddine Mballo, Robert Walters, Jonnalagadda V. R. Prasad
This paper presents the development of a novel life-extending control scheme for critical helicopter components subjected to significant fatigue loading. The primary objective is t…
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…
Granular clogging across gravities: a unified scaling
Oliver Gaida, Olfa D'Angelo, Jonathan E. Kollmer
Lacking a universal law for granular flows across gravitational environments, fundamental processes such as hopper discharge remain vulnerable to failure in low gravity environment…
Style Transfer with Diffusion Models for Synthetic-to-Real Domain Adaptation
Estelle Chigot, Dennis G. Wilson, Meriem Ghrib +1
Semantic segmentation models trained on synthetic data often perform poorly on real-world images due to domain gaps, particularly in adverse conditions where labeled data is scarce…
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…
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…
Caract{é}risation d'une Source Diffuse {à} partir des Moments de sa Densit{é} de Puissance en Tomographie SAR
Colin Cros, Laurent Ferro-Famil
This paper presents a method for estimating the characteristics of a diffuse source from interferometric measurements in the context of SAR tomography. The proposed method is based…
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…
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…
A linearly-implicit energy-momentum preserving scheme for geometrically nonlinear mechanics based on non-canonical Hamiltonian formulations
Andrea Brugnoli, Denis Matignon, Joseph Morlier
This work presents a novel formulation and numerical strategy for the simulation of geometrically nonlinear structures. First, a non-canonical Hamiltonian (Poisson) formulation is…