2 citations · 2 across the 3 of their papers we have counts for
4 papers
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…
A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace
Adam Keane, Nick Pepper, Chris Burr +4
Digital Twins combine simulation, operational data and Artificial Intelligence (AI), and have the potential to bring significant benefits across the aviation industry. Project Blue…
A Probabilistic Digital Twin of UK En Route Airspace for Training and Evaluating AI Agents for Air Traffic Control
Nick Pepper, Adam Keane, Amy Hodgkin +11
This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital Twin is intended to support the…
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…