dc.creatorNieto-Chaupis, Huber
dc.date.accessioned2023-10-04T19:01:49Z
dc.date.accessioned2024-08-06T21:07:37Z
dc.date.available2023-10-04T19:01:49Z
dc.date.available2024-08-06T21:07:37Z
dc.date.created2023-10-04T19:01:49Z
dc.date.issued2022
dc.identifierhttps://hdl.handle.net/20.500.13067/2659
dc.identifier2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC)
dc.identifierhttps://doi.org/10.1109/CEFC55061.2022.9940776
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9539662
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.
dc.languageeng
dc.publisherIEEE
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectQuantum computing
dc.subjectQuantization (signal)
dc.subjectElectromagnetic scattering
dc.subjectQuantum mechanics
dc.subjectMachine learning
dc.subjectMathematical models
dc.subjectElectromagnetic fields
dc.titleThe Criteria of Mitchell to Interpret Classical Radiation as Compton Scattering
dc.typeinfo:eu-repo/semantics/article


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