Testing Machine Learning at Classical Electrodynamics

Descripción del Articulo

Like physics or another laws-based basic science, machine learning might also be a firm methodology to solve physics problems by the which a kind of optimization and minimization of energy are needed. Expressed at the Mitchell's principles, machine learning can be seen as a strategy that allows...

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Detalles Bibliográficos
Autor: Nieto-Chaupis, Huber
Formato: artículo
Fecha de Publicación:2021
Institución:Universidad Autónoma del Perú
Repositorio:AUTONOMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.autonoma.edu.pe:20.500.13067/1647
Enlace del recurso:https://hdl.handle.net/20.500.13067/1647
https://doi.org/10.23919/SpliTech52315.2021.9566432
Nivel de acceso:acceso restringido
Materia:Electrodynamics
Atmospheric measurements
Volume measurement
Machine learning
Tools
Particle measurements
Minimization
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spelling Nieto-Chaupis, Huber2022-02-22T14:03:04Z2022-02-22T14:03:04Z2021-10-22Nieto-Chaupis, H. (2021, September). Testing Machine Learning at Classical Electrodynamics. In 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1-5). IEEE.978-953-290-112-2https://hdl.handle.net/20.500.13067/16472021 6th International Conference on Smart and Sustainable Technologies, SpliTech 2021https://doi.org/10.23919/SpliTech52315.2021.9566432Like physics or another laws-based basic science, machine learning might also be a firm methodology to solve physics problems by the which a kind of optimization and minimization of energy are needed. Expressed at the Mitchell's principles, machine learning can be seen as a strategy that allows to improve physical actions such as observation and measurement. In the classical territory, one can project the well-known electrodynamics over the steps: (i) task, (ii) performance, and (iii) experience. With this one might to guarantee a kind of learning to face a next similar situation and so on. This paper try to solve the problem of a charged particle inside a cylindrical volume but emphasizing its energy and its measurement. Simulations have shown that machine learning can also be an alternative tool to solve physics problems that require of minimization of energy.application/pdfengInstitute of Electrical and Electronics EngineersPEinfo:eu-repo/semantics/restrictedAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/AUTONOMA15reponame:AUTONOMA-Institucionalinstname:Universidad Autónoma del Perúinstacron:AUTONOMAElectrodynamicsAtmospheric measurementsVolume measurementMachine learningToolsParticle measurementsMinimizationhttps://purl.org/pe-repo/ocde/ford#2.02.04Testing Machine Learning at Classical Electrodynamicsinfo:eu-repo/semantics/articlehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118449716&doi=10.23919%2fSpliTech52315.2021.9566432&partnerID=LICENSElicense.txtlicense.txttext/plain; charset=utf-885http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1647/2/license.txt9243398ff393db1861c890baeaeee5f9MD52ORIGINALTesting Machine Learning at Classical Electrodynamics.pdfTesting Machine Learning at Classical Electrodynamics.pdfVer fuenteapplication/pdf98300http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1647/3/Testing%20Machine%20Learning%20at%20Classical%20Electrodynamics.pdfb65999a8be581ed45f495b69a0a52662MD53TEXTTesting Machine Learning at Classical Electrodynamics.pdf.txtTesting Machine Learning at Classical Electrodynamics.pdf.txtExtracted texttext/plain548http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1647/4/Testing%20Machine%20Learning%20at%20Classical%20Electrodynamics.pdf.txt5bf96f40d79045fb72987d8218be2612MD54THUMBNAILTesting Machine Learning at Classical Electrodynamics.pdf.jpgTesting Machine Learning at Classical Electrodynamics.pdf.jpgGenerated Thumbnailimage/jpeg5731http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1647/5/Testing%20Machine%20Learning%20at%20Classical%20Electrodynamics.pdf.jpg0c18e0e0fa2057b9bd20e7384c9065bbMD5520.500.13067/1647oai:repositorio.autonoma.edu.pe:20.500.13067/16472022-02-23 03:00:19.52Repositorio de la Universidad Autonoma del Perúrepositorio@autonoma.pe
dc.title.es_PE.fl_str_mv Testing Machine Learning at Classical Electrodynamics
title Testing Machine Learning at Classical Electrodynamics
spellingShingle Testing Machine Learning at Classical Electrodynamics
Nieto-Chaupis, Huber
Electrodynamics
Atmospheric measurements
Volume measurement
Machine learning
Tools
Particle measurements
Minimization
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short Testing Machine Learning at Classical Electrodynamics
title_full Testing Machine Learning at Classical Electrodynamics
title_fullStr Testing Machine Learning at Classical Electrodynamics
title_full_unstemmed Testing Machine Learning at Classical Electrodynamics
title_sort Testing Machine Learning at Classical Electrodynamics
author Nieto-Chaupis, Huber
author_facet Nieto-Chaupis, Huber
author_role author
dc.contributor.author.fl_str_mv Nieto-Chaupis, Huber
dc.subject.es_PE.fl_str_mv Electrodynamics
Atmospheric measurements
Volume measurement
Machine learning
Tools
Particle measurements
Minimization
topic Electrodynamics
Atmospheric measurements
Volume measurement
Machine learning
Tools
Particle measurements
Minimization
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description Like physics or another laws-based basic science, machine learning might also be a firm methodology to solve physics problems by the which a kind of optimization and minimization of energy are needed. Expressed at the Mitchell's principles, machine learning can be seen as a strategy that allows to improve physical actions such as observation and measurement. In the classical territory, one can project the well-known electrodynamics over the steps: (i) task, (ii) performance, and (iii) experience. With this one might to guarantee a kind of learning to face a next similar situation and so on. This paper try to solve the problem of a charged particle inside a cylindrical volume but emphasizing its energy and its measurement. Simulations have shown that machine learning can also be an alternative tool to solve physics problems that require of minimization of energy.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2022-02-22T14:03:04Z
dc.date.available.none.fl_str_mv 2022-02-22T14:03:04Z
dc.date.issued.fl_str_mv 2021-10-22
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.citation.es_PE.fl_str_mv Nieto-Chaupis, H. (2021, September). Testing Machine Learning at Classical Electrodynamics. In 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1-5). IEEE.
dc.identifier.isbn.none.fl_str_mv 978-953-290-112-2
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.13067/1647
dc.identifier.journal.es_PE.fl_str_mv 2021 6th International Conference on Smart and Sustainable Technologies, SpliTech 2021
dc.identifier.doi.none.fl_str_mv https://doi.org/10.23919/SpliTech52315.2021.9566432
identifier_str_mv Nieto-Chaupis, H. (2021, September). Testing Machine Learning at Classical Electrodynamics. In 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1-5). IEEE.
978-953-290-112-2
2021 6th International Conference on Smart and Sustainable Technologies, SpliTech 2021
url https://hdl.handle.net/20.500.13067/1647
https://doi.org/10.23919/SpliTech52315.2021.9566432
dc.language.iso.es_PE.fl_str_mv eng
language eng
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dc.publisher.es_PE.fl_str_mv Institute of Electrical and Electronics Engineers
dc.publisher.country.es_PE.fl_str_mv PE
dc.source.es_PE.fl_str_mv AUTONOMA
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Testing Machine Learning at Classical Electrodynamics
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