A Data-Driven Approach to Estimate LEO Orbit Capacity Models
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916863926075392 |
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| author | Stock, Braden McVarthy, Maddox Servadio, Simone |
| author_facet | Stock, Braden McVarthy, Maddox Servadio, Simone |
| contents | Utilizing the Sparse Identification of Nonlinear Dynamics algorithm (SINDy) and Long Short-Term Memory Recurrent Neural Networks (LSTM), the population of resident space objects, divided into Active, Derelict, and Debris, in LEO can be accurately modeled to predict future satellite and debris propagation. This proposed approach makes use of a data set coming from a computational expensive high-fidelity model, the MOCAT-MC, to provide a light, low-fidelity counterpart that provides accurate forecasting in a shorter time frame. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19365 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Data-Driven Approach to Estimate LEO Orbit Capacity Models Stock, Braden McVarthy, Maddox Servadio, Simone Machine Learning Utilizing the Sparse Identification of Nonlinear Dynamics algorithm (SINDy) and Long Short-Term Memory Recurrent Neural Networks (LSTM), the population of resident space objects, divided into Active, Derelict, and Debris, in LEO can be accurately modeled to predict future satellite and debris propagation. This proposed approach makes use of a data set coming from a computational expensive high-fidelity model, the MOCAT-MC, to provide a light, low-fidelity counterpart that provides accurate forecasting in a shorter time frame. |
| title | A Data-Driven Approach to Estimate LEO Orbit Capacity Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.19365 |