From perturbative methods to machine learning techniques in space science

Perturbation theory is a very useful tool to investigate the dynamics of models in space science. We start by presenting some results obtained implementing classical perturbation theory to investigate the motion of space debris, which are objects that populate the sky around the Earth after a satell...

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Published inBollettino della Unione matematica italiana (2008) Vol. 18; no. 1; pp. 149 - 166
Main Author Celletti, Alessandra
Format Journal Article
LanguageEnglish
Published Heidelberg Springer Nature B.V 01.03.2025
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ISSN1972-6724
2198-2759
DOI10.1007/s40574-024-00422-x

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Abstract Perturbation theory is a very useful tool to investigate the dynamics of models in space science. We start by presenting some results obtained implementing classical perturbation theory to investigate the motion of space debris, which are objects that populate the sky around the Earth after a satellite break-up event. When dealing with two or more break-up events, a clusterization of the fragments can be computed using machine learning techniques. We also present the celebrated KAM theory for symplectic and conformally symplectic systems. We recall several computer-assisted results in Celestial Mechanics in conservative and dissipative settings. Finally, we consider the spin-orbit problem and we show how machine learning methods can be conveniently used to classify regular and chaotic motions.
AbstractList Perturbation theory is a very useful tool to investigate the dynamics of models in space science. We start by presenting some results obtained implementing classical perturbation theory to investigate the motion of space debris, which are objects that populate the sky around the Earth after a satellite break-up event. When dealing with two or more break-up events, a clusterization of the fragments can be computed using machine learning techniques. We also present the celebrated KAM theory for symplectic and conformally symplectic systems. We recall several computer-assisted results in Celestial Mechanics in conservative and dissipative settings. Finally, we consider the spin-orbit problem and we show how machine learning methods can be conveniently used to classify regular and chaotic motions.
Author Celletti, Alessandra
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Snippet Perturbation theory is a very useful tool to investigate the dynamics of models in space science. We start by presenting some results obtained implementing...
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SubjectTerms Celestial mechanics
Machine learning
Orbital mechanics
Perturbation theory
Space debris
Spacecraft breakup
Title From perturbative methods to machine learning techniques in space science
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