A Machine Learning-Based Thermospheric Density Model with Uncertainty Quantification
Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysi...
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| Main Authors: | , , |
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| Format: | Artigo |
| Language: | Inglês |
| Published: |
MDPI AG
2025-09-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/10/1120 |
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