Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management
Abstract
Accurate thermospheric density prediction is crucial for reliable satellite operations in Low Earth Orbits, especially at high solar and geomagnetic activity. Physics-based models such as TIE-GCM offer high fidelity but are computationally expensive, while empirical models like NRLMSIS are efficient yet lack predictive power. This work presents a transformer-based model that forecasts densities up to three days ahead and is intended as a drop-in replacement for an empirical baseline. Unlike recent approaches, it avoids spatial reduction and complex input pipelines, operating directly on a compact input set. Validated on real-world data, the model improves key prediction metrics and shows potential to support mission planning.
Cite
@article{arxiv.2511.06105,
title = {Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management},
author = {Cedric Bös and Alessandro Bortotto and Mohamed Khalil Ben-Larbi},
journal= {arXiv preprint arXiv:2511.06105},
year = {2026}
}
Comments
6 pages, 3 figures, conference