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dc.contributor.authorNieto-Chaupis, Huber
dc.date.accessioned2025-02-26T19:50:42Z
dc.date.available2025-02-26T19:50:42Z
dc.date.issued2025-02-26
dc.identifier.urihttps://hdl.handle.net/20.500.13067/3682
dc.description.abstractBased at the prospective scenario that computers might to replace humans, emerge the idea that them can carry out jobs as a scientist does. Thus, one might to expect that for example machine learning can be a serious competitor of humans as to produce research even of cutting edge quality. In this paper, some algorithms based at artificial neural networks are employed to produce known physics based at coincidences with artificial models generated by Machine Learning approach.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherIEEEes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_PE
dc.subjectMachine learninges_PE
dc.subjectPerceptrones_PE
dc.subjectPhysicses_PE
dc.titleMachine Learning Algorithms for Producing Equations in Physicses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journalIEEEes_PE
dc.identifier.doihttps://doi.org/10.1109/ICECCE63537.2024.10823410
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.publisher.countryPEes_PE
dc.source.beginpage1es_PE
dc.source.endpage5es_PE


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