Near-Earth asteroid orbit determination with physics-informed extreme learning machine
- Autores
- Chen, X.; Tang, K.; Zhang, Q.; Tian, Z.; Yu, Y.
- Año de publicación
- 2025
- Idioma
- español castellano
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Despite the growing enthusiasm for AI in space applications, AI-based orbit determination is still a relatively new field with limited practical implementations [1-3]. A promising new development is Physics-Informed Extreme Learning Machine (PIELM) [4], It combines the rapid training capabilities of Extreme Learning Machines (ELM) with the physics-informed strengths of Physics-Informed Neural Networks (FINN), which ensures solutions consistent with physical laws and actual measurements. This synergy makes PIELM well-suited for solving the complex orbit determination problem.
Facultad de Ciencias Astronómicas y Geofísicas - Materia
-
Ciencias Astronómicas
Extreme Learning Machines
Physics-Informed Neural Networks
Asteroids
Orbits - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-nd/4.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/192183
Ver los metadatos del registro completo
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Near-Earth asteroid orbit determination with physics-informed extreme learning machineChen, X.Tang, K.Zhang, Q.Tian, Z.Yu, Y.Ciencias AstronómicasExtreme Learning MachinesPhysics-Informed Neural NetworksAsteroidsOrbitsDespite the growing enthusiasm for AI in space applications, AI-based orbit determination is still a relatively new field with limited practical implementations [1-3]. A promising new development is Physics-Informed Extreme Learning Machine (PIELM) [4], It combines the rapid training capabilities of Extreme Learning Machines (ELM) with the physics-informed strengths of Physics-Informed Neural Networks (FINN), which ensures solutions consistent with physical laws and actual measurements. This synergy makes PIELM well-suited for solving the complex orbit determination problem.Facultad de Ciencias Astronómicas y Geofísicas2025-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionResumenhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/192183spainfo:eu-repo/semantics/reference/url/https://sedici.unlp.edu.ar/handle/10915/190232info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2026-03-31T12:42:01Zoai:sedici.unlp.edu.ar:10915/192183Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292026-03-31 12:42:01.898SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Near-Earth asteroid orbit determination with physics-informed extreme learning machine |
| title |
Near-Earth asteroid orbit determination with physics-informed extreme learning machine |
| spellingShingle |
Near-Earth asteroid orbit determination with physics-informed extreme learning machine Chen, X. Ciencias Astronómicas Extreme Learning Machines Physics-Informed Neural Networks Asteroids Orbits |
| title_short |
Near-Earth asteroid orbit determination with physics-informed extreme learning machine |
| title_full |
Near-Earth asteroid orbit determination with physics-informed extreme learning machine |
| title_fullStr |
Near-Earth asteroid orbit determination with physics-informed extreme learning machine |
| title_full_unstemmed |
Near-Earth asteroid orbit determination with physics-informed extreme learning machine |
| title_sort |
Near-Earth asteroid orbit determination with physics-informed extreme learning machine |
| dc.creator.none.fl_str_mv |
Chen, X. Tang, K. Zhang, Q. Tian, Z. Yu, Y. |
| author |
Chen, X. |
| author_facet |
Chen, X. Tang, K. Zhang, Q. Tian, Z. Yu, Y. |
| author_role |
author |
| author2 |
Tang, K. Zhang, Q. Tian, Z. Yu, Y. |
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author author author author |
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Ciencias Astronómicas Extreme Learning Machines Physics-Informed Neural Networks Asteroids Orbits |
| topic |
Ciencias Astronómicas Extreme Learning Machines Physics-Informed Neural Networks Asteroids Orbits |
| dc.description.none.fl_txt_mv |
Despite the growing enthusiasm for AI in space applications, AI-based orbit determination is still a relatively new field with limited practical implementations [1-3]. A promising new development is Physics-Informed Extreme Learning Machine (PIELM) [4], It combines the rapid training capabilities of Extreme Learning Machines (ELM) with the physics-informed strengths of Physics-Informed Neural Networks (FINN), which ensures solutions consistent with physical laws and actual measurements. This synergy makes PIELM well-suited for solving the complex orbit determination problem. Facultad de Ciencias Astronómicas y Geofísicas |
| description |
Despite the growing enthusiasm for AI in space applications, AI-based orbit determination is still a relatively new field with limited practical implementations [1-3]. A promising new development is Physics-Informed Extreme Learning Machine (PIELM) [4], It combines the rapid training capabilities of Extreme Learning Machines (ELM) with the physics-informed strengths of Physics-Informed Neural Networks (FINN), which ensures solutions consistent with physical laws and actual measurements. This synergy makes PIELM well-suited for solving the complex orbit determination problem. |
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2025 |
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2025-08 |
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