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dc.contributor.authorNieto-Chaupis, Huber
dc.date.accessioned2025-03-13T16:20:12Z
dc.date.available2025-03-13T16:20:12Z
dc.date.issued2025-03-13
dc.identifier.urihttps://hdl.handle.net/20.500.13067/3722
dc.description.abstractMachine Learning has been used as part of a closed-form operation that allows to link classical physics to quantum mechanics of a free electron radiating photons inside superintese laser field. To accomplish this an engineered perceptron has been constructed. Thus, the usage of integer-order Bessel functions would play a relevant role. The criterion suggested by Ritus and Nikishov have could to end at the construction of a model that allows to estimate quantized energy. The proposal perceptron might be an effective generator of quantum mechanics observables depending on weights as well as the specific operation of activation. From the results, one would arrive to suggest the idea Machine Learning might to play the role as an effective constructor of quantum theories without any knowledge of it, previously.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 as Mediator of Classical Electrodynamics and Quantum Mechanicses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journal2024 IEEE 22nd Student Conference on Research and Development (SCOReD)es_PE
dc.identifier.doihttps://doi.org/10.1109/APACE62360.2024.10877319
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.publisher.countryPEes_PE
dc.source.beginpage327es_PE
dc.source.endpage330es_PE


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