44 citations · 50 across the 3 of their papers we have counts for
3 papers
Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment
Oskar Bohn Lassen, Serio Agriesti, Mohamed Eldafrawi +4
The Traffic Assignment Problem is a fundamental, yet computationally expensive, task in transportation modeling, especially for large-scale networks. Traditional methods require it…
Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling
Davide Celestini, Daniele Gammelli, Tommaso Guffanti +3
Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. H…
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Daniele Gammelli, James Harrison, Kaidi Yang +3
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional op…