21 citations · 52 across the 27 of their papers we have counts for
6 papers · 1 filter
IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics
Halil S. Kelebek, Linnea M. Wolniewicz, Michael D. Vergalla +8
The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting a…
Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers
Giacomo Acciarini, Simone Mestici, Halil Kelebek +7
The ionosphere critically influences Global Navigation Satellite Systems (GNSS), satellite communications, and Low Earth Orbit (LEO) operations, yet accurate prediction of its vari…
Towards Methane Detection Onboard Satellites
Maggie Chen, Hala Lamdouar, Luca Marini +3
Methane is a potent greenhouse gas and a major driver of climate change, making its timely detection critical for effective mitigation. Machine learning (ML) deployed onboard satel…
Probability of collision in nonlinear dynamics by moment propagation
Théo Verhelst, Giacomo Acciarini, Dario Izzo +1
Estimating the probability of collision between spacecraft is crucial for risk management and collision-avoidance strategies. Current methods often rely on Gaussian assumptions and…
EclipseNETs: Learning Irregular Small Celestial Body Silhouettes
Giacomo Acciarini, Dario Izzo, Francesco Biscani
Accurately predicting eclipse events around irregular small bodies is crucial for spacecraft navigation, orbit determination, and spacecraft systems management. This paper introduc…
High-order expansion of Neural Ordinary Differential Equations flows
Dario Izzo, Sebastien Origer, Giacomo Acciarini +1
Artificial neural networks, widely recognised for their role in machine learning, are now transforming the study of ordinary differential equations (ODEs), bridging data-driven mod…