Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910077342973952 |
|---|---|
| author | Bös, Cedric Bortotto, Alessandro Ben-Larbi, Mohamed Khalil |
| author_facet | Bös, Cedric Bortotto, Alessandro Ben-Larbi, Mohamed Khalil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06105 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management Bös, Cedric Bortotto, Alessandro Ben-Larbi, Mohamed Khalil Space Physics Machine Learning 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. |
| title | Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management |
| topic | Space Physics Machine Learning |
| url | https://arxiv.org/abs/2511.06105 |