{"ID":2844703,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06105","arxiv_id":"2511.06105","title":"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.","short_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...","url_abs":"https://arxiv.org/abs/2511.06105","url_pdf":"https://arxiv.org/pdf/2511.06105v1","authors":"[\"Cedric Bös\",\"Alessandro Bortotto\",\"Mohamed Khalil Ben-Larbi\"]","published":"2025-11-08T19:02:14Z","proceeding":"physics.space-ph","tasks":"[\"physics.space-ph\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}