Nationality: Iranian
PhD Thesis
Network-Level Aircraft Trajectory Optimization for Climate Impact Mitigation using Multi-Agent Reinforcement Learning
Date of Defense: 26 November 2025
Supervisors
Manuel Fernando Soler Arnedo (UC3M)
Abstract
Aviation contributes to human-induced climate change through the emission of carbon dioxide (CO2) and other non-CO2 forcing agents. The latter, responsible for roughly two-thirds of aviation’s climate effects, is highly sensitive to the time and location of emissions. Consequently, operational measures such as climate-aware flight planning offer a practical and infrastructure-compatible solution to mitigate aviation’s climate impact in the short term. However, optimizing flight paths individually can compromise air traffic safety and manageability due to the consideration of climate-sensitive zones. My thesis introduces frameworks based on deep multi-agent reinforcement learning to reconcile climate-optimal flight trajectories with the operational requirements of the air traffic management system, enabling the deployment of feasible, climate-optimal aircraft trajectories.
Awards
Luis Azcárraga Award at the 28th edition of the ENAIRE Foundation Aeronautical Awards (2023)
REFMAP – Reducing Environmental Footprint through transformative Multi-scale Aviation Planning by the European Commission under Grant 101096698
Research Stays
Institutions: École Polytechnique Fédérale de Lausanne (EPFL) (Switzerland)
Department/Group: SYCAMORE Lab
Host: Maryam Kamgarpour
Period: from April 2024 to July 2024
PhD in Aerospace Engineering UC3M 