Seminar Details
2026-02-17 (14:00) : Newcomers Seminar
At Euler building (room A.002)
Organized by Mathematical Engineering
Section 1: Fleet Rebalancing: Scalable Feedback Optimization for Autonomous Mobility-on-Demand via null-space projection
Speaker :
Arthur Mélot (UCLouvain)
Abstract :
While state-of-the-art Model Predictive Control (MPC) approaches for Autonomous Mobility-on-Demand (AMoD) achieve scalability through flow-based modeling , they remain computationally burdened by iterative solvers , the need for a pre-computed static equilibrium to track and also a non-negligible prediction horizon to ensure stability. This presentation introduces a horizon-free feedback optimization algorithm that also eliminates the need for offline equilibrium computation. By treating the economic cost minimization as a dynamic feedback process, our approach steers the fleet directly toward the optimal operating point in real-time and does not track a pre-computed reference signal. The preliminary results provide a negligible execution time, outperforming the execution time of standard MPC. This project is still in progress and all the results provided are preliminary results.
Section 2: Mechanical determinants of tactile perception in the human fingertip
Speaker :
Viktoriia Kozadaeva (UCLouvain)
Abstract :
Human tactile perception enables discrimination of features with microscale precision. This remarkable sensitivity arises from dense innervation of mechanoreceptors within the fingertip that transduce small skin deformations into neural signals. While neural innervation of the fingertip has been widely studied, the mechanical mechanisms enabling the microscale sensations remain underexplored. My research project aims to establish a relationship between psychophysical performance and mechanical determinants underlying microscale tactile perception. Specifically, we examine how fingerpad deformation relates to perceptual detection of small surface features.
Section 3: Reinforcement Learning for Petri-Net based Discrete Event Systems: Application to Aircraft Maintenance Scheduling
Speaker :
Margot Devillers (UCLouvain)
Abstract :
Efficient aircraft maintenance scheduling is critical to maximizing fleet availability while ensuring regulatory compliance and cost efficiency. The problem involves grouping periodic tasks into maintenance projects and deciding when to ground aircraft under strict deadline and resource constraints. The combinatorial nature of these decisions makes classical optimization approaches challenging at fleet scale.
Building on a Colored Petri Net (CPN) digital twin of the aircraft maintenance operations, this work formulates the scheduling process as a discrete-event dynamical system and investigates the use of Reinforcement Learning (RL) to address it. An RL agent is currently being developed to interact with the CPN environment and learn fleet-level policies that balance aircraft utilization and operational feasibility over a finite planning horizon.