Institution profile

Institut Supérieur de l'Aéronautique et de l'Espace

Academic institutioneurope · fr
Official website
Research library43linked papers
Opportunities0open roles
Selected work

Representative Papers

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

Apr 11, 2025

In multidisciplinary design optimization—particularly aircraft design—the presence of high-dimensional mixed variables (continuous, integer, and categorical) causes combinatorial explosion in the hyperparameter space of Bayesian optimization surrogate models. To address this, we propose a Partial Least Squares (PLS)-based adaptive dimensionality reduction framework. It dynamically learns variable coupling structures to significantly compress the surrogate hyperparameter space, while integrating mixed-variable encoding with an adaptive hyperparameter selection mechanism to balance modeling accuracy and tuning efficiency. Evaluated on analytical benchmarks and two real-world aircraft design cases, our method achieves >30% faster convergence and improves optimal solution quality by 12–18% compared to genetic algorithms, while reducing hyperparameter count by ~60%. Our core contribution is the first application of PLS for hyperparameter pruning in Bayesian optimization, enabling efficient and robust optimization under high-dimensional mixed-variable settings.

8 citations3 influentialRead paper

Constrained Multi-Objective Bayesian Optimization with Application to Aircraft Design

Jun 20, 2022AIAA AVIATION 2022 Forum

Bayesian optimization (BO) methods for computationally expensive, nonlinearly constrained multi-objective optimization problems—such as aircraft conceptual design—often suffer from ill-conditioning in multi-objective acquisition functions, leading to unstable surrogate updates and poor convergence. Method: This paper extends the SEGOMOE framework by introducing a novel regularization mechanism directly into the multi-objective acquisition function, synergistically integrating Kriging surrogates, a Mixture-of-Experts (MoE) architecture, and an enhanced SEGO algorithm. Contribution/Results: The proposed approach systematically alleviates the trade-off between ill-conditioning and convergence in constrained multi-objective BO. Empirical evaluation on aircraft design tasks demonstrates that it achieves high-quality Pareto fronts using only 5% of the function evaluations required by NSGA-II, significantly improving the efficiency of identifying high-fidelity, low-cost compromise solutions.

8 citationsRead paper

FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets

Jul 28, 2026

Current drone swarm control interfaces rely on single-vehicle interaction paradigms, which struggle to meet the complex supervisory demands of large-scale formations across spatial, temporal, and safety dimensions. This work reframes swarm control as a spatial interaction task and introduces FleetScape, a novel mixed reality (MR) sandbox system that externalizes multidimensional data—such as mission status, environmental context, and safety constraints—into spatialized visualizations. FleetScape enables seamless transitions between manual intervention and autonomous supervision, integrating high-fidelity building inspection simulation, synchronized multi-drone–environment data streams, and spatial interaction mechanisms. User studies demonstrate that FleetScape significantly enhances operators’ situational awareness and clarifies transitions between control modes; however, as swarm size increases, situational awareness degrades, prompting users to adapt their supervisory strategies.

0 citationsRead paper
Recent publications

Latest Papers

FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets

Jul 28, 2026

Current drone swarm control interfaces rely on single-vehicle interaction paradigms, which struggle to meet the complex supervisory demands of large-scale formations across spatial, temporal, and safety dimensions. This work reframes swarm control as a spatial interaction task and introduces FleetScape, a novel mixed reality (MR) sandbox system that externalizes multidimensional data—such as mission status, environmental context, and safety constraints—into spatialized visualizations. FleetScape enables seamless transitions between manual intervention and autonomous supervision, integrating high-fidelity building inspection simulation, synchronized multi-drone–environment data streams, and spatial interaction mechanisms. User studies demonstrate that FleetScape significantly enhances operators’ situational awareness and clarifies transitions between control modes; however, as swarm size increases, situational awareness degrades, prompting users to adapt their supervisory strategies.

0 citationsRead paper

Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning

Jul 07, 2026

This study addresses the problem of cooperative interception of highly maneuverable targets by multiple agile drones equipped with capture nets, formulated as a competitive multi-agent reinforcement learning task. The proposed approach employs the MAPPO algorithm integrated with Prioritized Fictitious Self-Play (PFSP) to train policies that directly output low-level control commands—namely collective thrust and body-frame angular rates—enabling end-to-end learning in a high-fidelity simulation environment. This method effectively mitigates non-stationarity and catastrophic forgetting, significantly outperforming heuristic baselines in terms of higher capture success rate, shorter interception time, and lower collision frequency. Ablation studies confirm the critical contributions of PFSP and low-level control, while emergent cooperative tactics among pursuers are observed during training.

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A Methodology for Integrating Life Cycle Assessment into a Multidisciplinary Design Analysis and Optimization Framework for Sustainable Launcher Development

Jun 24, 2026

This study addresses the lack of systematic assessment and multidisciplinary co-optimization of environmental impacts across the full life cycle in early-stage launch vehicle design. It presents the first deep integration of life cycle assessment (LCA) into a multidisciplinary design analysis and optimization (MDAO) framework. By coupling a parameterized life cycle inventory with a trajectory simulation-driven launch emissions model, the approach quantifies climate change impacts from manufacturing, propellant production, transportation, and launch phases. A multi-objective optimization is then performed to balance vehicle performance against multiple environmental indicators. Case studies demonstrate that the method effectively uncovers trade-offs among environmental metrics and enables informed co-optimization of performance, cost, and sustainability, offering a general and practical decision-support tool for greener spacecraft design.

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