Artificial intelligence and machine learning methods in celestial mechanics
| dc.contributor.author | Carruba, Valerio [UNESP] | |
| dc.contributor.author | Smirnov, Evgeny | |
| dc.contributor.author | Caritá, Gabriel | |
| dc.contributor.author | Oszkiewicz, Dagmara | |
| dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
| dc.contributor.institution | Belgrade Astronomical Observatory | |
| dc.contributor.institution | Division of Graduate Studies | |
| dc.contributor.institution | Adam Mickiewicz University | |
| dc.date.accessioned | 2025-04-29T20:11:28Z | |
| dc.date.issued | 2024-01-01 | |
| dc.description.abstract | The astronomical field is entering the big data science era as a result of the rapid expansion of astronomical datasets' quantity and complexity. The sheer magnitude of contemporary astronomical datasets makes the employment of techniques other than human researcher eye examination necessary. Machine learning (ML) is the study and creation of algorithms that can learn from data. The term artificial intelligence (AI) describes the replication of human intelligence in machines that have been designed to have human-like thought and learning processes. In this chapter, we will briefly revise some of the most commonly used algorithms for application to Solar System small bodies, and provide references and links to readers interested in learning about their use in Astronomy. | en |
| dc.description.affiliation | São Paulo State University (UNESP) Department of Mathematics, SP | |
| dc.description.affiliation | Belgrade Astronomical Observatory | |
| dc.description.affiliation | National Institute for Space and Research (INPE) Division of Graduate Studies, SP | |
| dc.description.affiliation | Astronomical Observatory Institute Faculty of Physics and Astronomy Adam Mickiewicz University | |
| dc.description.affiliationUnesp | São Paulo State University (UNESP) Department of Mathematics, SP | |
| dc.format.extent | 1-32 | |
| dc.identifier | http://dx.doi.org/10.1016/B978-0-44-324770-5.00006-4 | |
| dc.identifier.citation | Machine Learning for Small Bodies in the Solar System, p. 1-32. | |
| dc.identifier.doi | 10.1016/B978-0-44-324770-5.00006-4 | |
| dc.identifier.scopus | 2-s2.0-85214182180 | |
| dc.identifier.uri | https://hdl.handle.net/11449/308181 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Machine Learning for Small Bodies in the Solar System | |
| dc.source | Scopus | |
| dc.subject | Astronomical data bases: miscellaneous | |
| dc.subject | Minor planets, asteroids: general | |
| dc.subject | Minor planets, asteroids: individual | |
| dc.title | Artificial intelligence and machine learning methods in celestial mechanics | en |
| dc.type | Capítulo de livro | pt |
| dspace.entity.type | Publication |

