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dc.contributor.authorNieto-Chaupis, Huber
dc.date.accessioned2023-10-04T19:01:49Z
dc.date.available2023-10-04T19:01:49Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/20.500.13067/2659
dc.description.abstractThe principles of Machine Learning through the criteria of Mitchell are used to validate a concrete quantum-mechanics interpretation from a classical radiation scheme inside the framework of linear and nonlinear Compton scattering off a relativistic electron in a super-intense electromagnetic field.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-nc-nd/4.0/es_PE
dc.subjectQuantum computinges_PE
dc.subjectQuantization (signal)es_PE
dc.subjectElectromagnetic scatteringes_PE
dc.subjectQuantum mechanicses_PE
dc.subjectMachine learninges_PE
dc.subjectMathematical modelses_PE
dc.subjectElectromagnetic fieldses_PE
dc.titleThe Criteria of Mitchell to Interpret Classical Radiation as Compton Scatteringes_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journal2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)es_PE
dc.identifier.doihttps://doi.org/10.1109/CEFC55061.2022.9940776
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE


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