Artificial intelligence and machine learning methods in celestial mechanics

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorBelgrade Astronomical Observatory-
Autor(es): dc.contributorDivision of Graduate Studies-
Autor(es): dc.contributorAdam Mickiewicz University-
Autor(es): dc.creatorCarruba, Valerio-
Autor(es): dc.creatorSmirnov, Evgeny-
Autor(es): dc.creatorCaritá, Gabriel-
Autor(es): dc.creatorOszkiewicz, Dagmara-
Data de aceite: dc.date.accessioned2025-08-21T16:24:46Z-
Data de disponibilização: dc.date.available2025-08-21T16:24:46Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/B978-0-44-324770-5.00006-4-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308181-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308181-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Mathematics, SP-
Descrição: dc.descriptionBelgrade Astronomical Observatory-
Descrição: dc.descriptionNational Institute for Space and Research (INPE) Division of Graduate Studies, SP-
Descrição: dc.descriptionAstronomical Observatory Institute Faculty of Physics and Astronomy Adam Mickiewicz University-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Mathematics, SP-
Formato: dc.format1-32-
Idioma: dc.languageen-
Relação: dc.relationMachine Learning for Small Bodies in the Solar System-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAstronomical data bases: miscellaneous-
Palavras-chave: dc.subjectMinor planets, asteroids: general-
Palavras-chave: dc.subjectMinor planets, asteroids: individual-
Título: dc.titleArtificial intelligence and machine learning methods in celestial mechanics-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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