7 citations · 14 across the 14 of their papers we have counts for
4 papers · 1 filter
Conditioning Aircraft Trajectory Prediction on Meteorological Data with a Physics-Informed Machine Learning Approach
Amy Hodgkin, Nick Pepper, Marc Thomas
Accurate aircraft trajectory prediction (TP) in air traffic management systems is confounded by a number of epistemic uncertainties, dominated by uncertain meteorological condition…
Probabilistic Simulation of Aircraft Descent via a Physics-Informed Machine Learning Approach
Amy Hodgkin, Nick Pepper, Marc Thomas
This paper presents a method for generating probabilistic descent trajectories in simulations of real-world airspace. A dataset of 116,066 trajectories harvested from Mode S radar…
Context-Aware Generative Models for Prediction of Aircraft Ground Tracks
Nick Pepper, George De Ath, Marc Thomas +2
Trajectory prediction (TP) plays an important role in supporting the decision-making of Air Traffic Controllers (ATCOs). Traditional TP methods are deterministic and physics-based,…
Learning Generative Models for Climbing Aircraft from Radar Data
Nick Pepper, Marc Thomas
Accurate trajectory prediction (TP) for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspe…