Reduction of the computational cost of tuning methodology of a simulator of a physical system

Autores
Trigila, Mariano; Gaudiani, Adriana; Wong, Alvaro; Rexachs, Dolores; Luque, Emilio
Año de publicación
2023
Idioma
inglés
Tipo de recurso
parte de libro
Estado
versión publicada
Descripción
Fil: Trigila, Mariano. Pontificia Universidad Católica Argentina. Facultad de Ingeniería y Ciencias Agrarias; Argentina
Fil: Gaudiani, Adriana. Universidad Nacional de General Sarmiento. Instituto de Ciencias; Argentina
Fil: Wong, Alvaro. Universidad Autònoma de Barcelona. Departamento de Arquitectura de Computadores y Sistemas Operativos; España
Fil: Rexachs, Dolores. Universidad Autònoma de Barcelona. Departamento de Arquitectura de Computadores y Sistemas Operativos; España
Fil: Luque, Emilio. Universidad Autònoma de Barcelona. Departamento de Arquitectura de Computadores y Sistemas Operativos; España
Abstract: We propose a methodology for calibrating a physical system simulator and whose computational model represents its events in time series. The methodology reduces the search space of the fit parameters by exploring a database that contains stored historical events and their corresponding simulator fit parameters. We carry out the symbolic representation of the time series using ordinal patterns to classify the series, which allows us to search and compare by similarity on the stored data of the series represented. This classification strategy allows us to speed up the parameter search process, reduce the computational cost of the adjustment process and consequently improve energy cost savings. The experiences showed a reduction in the computational cost of 29% compared with our tuning methodology proposed in previous research.
Fuente
En: Mikyška, J. et al. (eds). Computational Science. Lecture notes in Computer Science, vol 10475. Cham : Springer, 2023.
Materia
SIMULACION PARAMETRICA
METODOLOGIA DE SINTONIZACION
PATRON ORDINAL
BASES DE DATOS
HERRAMIENTAS INFORMATICAS
SOFTWARE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Repositorio Institucional (UCA)
Institución
Pontificia Universidad Católica Argentina
OAI Identificador
oai:ucacris:123456789/17072
Acceder
id RIUCA_9b3dec761b59e19f3481911f0537dfac
oai_identifier_str oai:ucacris:123456789/17072
network_acronym_str RIUCA
repository_id_str 2585
network_name_str Repositorio Institucional (UCA)
spelling Reduction of the computational cost of tuning methodology of a simulator of a physical systemTrigila, MarianoGaudiani, AdrianaWong, AlvaroRexachs, DoloresLuque, EmilioSIMULACION PARAMETRICAMETODOLOGIA DE SINTONIZACIONPATRON ORDINALBASES DE DATOSHERRAMIENTAS INFORMATICASSOFTWAREFil: Trigila, Mariano. Pontificia Universidad Católica Argentina. Facultad de Ingeniería y Ciencias Agrarias; ArgentinaFil: Gaudiani, Adriana. Universidad Nacional de General Sarmiento. Instituto de Ciencias; ArgentinaFil: Wong, Alvaro. Universidad Autònoma de Barcelona. Departamento de Arquitectura de Computadores y Sistemas Operativos; EspañaFil: Rexachs, Dolores. Universidad Autònoma de Barcelona. Departamento de Arquitectura de Computadores y Sistemas Operativos; EspañaFil: Luque, Emilio. Universidad Autònoma de Barcelona. Departamento de Arquitectura de Computadores y Sistemas Operativos; EspañaAbstract: We propose a methodology for calibrating a physical system simulator and whose computational model represents its events in time series. The methodology reduces the search space of the fit parameters by exploring a database that contains stored historical events and their corresponding simulator fit parameters. We carry out the symbolic representation of the time series using ordinal patterns to classify the series, which allows us to search and compare by similarity on the stored data of the series represented. This classification strategy allows us to speed up the parameter search process, reduce the computational cost of the adjustment process and consequently improve energy cost savings. The experiences showed a reduction in the computational cost of 29% compared with our tuning methodology proposed in previous research.Springer2023info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_3248info:ar-repo/semantics/parteDeLibroapplication/pdfhttps://repositorio.uca.edu.ar/handle/123456789/17072978-3-031-36023-7 (impreso)978-3-031-36024-4 (online)10.1007/978-3-031-36024-4_49Trigila, M. et al. Reduction of the computational cost of tuning methodology of a simulator of a physical system [en línea]. En: Mikyška, J. et al. (eds). Computational Science. Lecture notes in Computer Science, vol 10475. Cham : Springer, 2023. doi: 10.1007/978-3-031-36024-4_49. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/17072En: Mikyška, J. et al. (eds). Computational Science. Lecture notes in Computer Science, vol 10475. Cham : Springer, 2023.reponame:Repositorio Institucional (UCA)instname:Pontificia Universidad Católica Argentinaenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/2025-07-03T10:59:29Zoai:ucacris:123456789/17072instacron:UCAInstitucionalhttps://repositorio.uca.edu.ar/Universidad privadaNo correspondehttps://repositorio.uca.edu.ar/oaiclaudia_fernandez@uca.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:25852025-07-03 10:59:30.259Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentinafalse
dc.title.none.fl_str_mv Reduction of the computational cost of tuning methodology of a simulator of a physical system
title Reduction of the computational cost of tuning methodology of a simulator of a physical system
spellingShingle Reduction of the computational cost of tuning methodology of a simulator of a physical system
Trigila, Mariano
SIMULACION PARAMETRICA
METODOLOGIA DE SINTONIZACION
PATRON ORDINAL
BASES DE DATOS
HERRAMIENTAS INFORMATICAS
SOFTWARE
title_short Reduction of the computational cost of tuning methodology of a simulator of a physical system
title_full Reduction of the computational cost of tuning methodology of a simulator of a physical system
title_fullStr Reduction of the computational cost of tuning methodology of a simulator of a physical system
title_full_unstemmed Reduction of the computational cost of tuning methodology of a simulator of a physical system
title_sort Reduction of the computational cost of tuning methodology of a simulator of a physical system
dc.creator.none.fl_str_mv Trigila, Mariano
Gaudiani, Adriana
Wong, Alvaro
Rexachs, Dolores
Luque, Emilio
author Trigila, Mariano
author_facet Trigila, Mariano
Gaudiani, Adriana
Wong, Alvaro
Rexachs, Dolores
Luque, Emilio
author_role author
author2 Gaudiani, Adriana
Wong, Alvaro
Rexachs, Dolores
Luque, Emilio
author2_role author
author
author
author
dc.subject.none.fl_str_mv SIMULACION PARAMETRICA
METODOLOGIA DE SINTONIZACION
PATRON ORDINAL
BASES DE DATOS
HERRAMIENTAS INFORMATICAS
SOFTWARE
topic SIMULACION PARAMETRICA
METODOLOGIA DE SINTONIZACION
PATRON ORDINAL
BASES DE DATOS
HERRAMIENTAS INFORMATICAS
SOFTWARE
dc.description.none.fl_txt_mv Fil: Trigila, Mariano. Pontificia Universidad Católica Argentina. Facultad de Ingeniería y Ciencias Agrarias; Argentina
Fil: Gaudiani, Adriana. Universidad Nacional de General Sarmiento. Instituto de Ciencias; Argentina
Fil: Wong, Alvaro. Universidad Autònoma de Barcelona. Departamento de Arquitectura de Computadores y Sistemas Operativos; España
Fil: Rexachs, Dolores. Universidad Autònoma de Barcelona. Departamento de Arquitectura de Computadores y Sistemas Operativos; España
Fil: Luque, Emilio. Universidad Autònoma de Barcelona. Departamento de Arquitectura de Computadores y Sistemas Operativos; España
Abstract: We propose a methodology for calibrating a physical system simulator and whose computational model represents its events in time series. The methodology reduces the search space of the fit parameters by exploring a database that contains stored historical events and their corresponding simulator fit parameters. We carry out the symbolic representation of the time series using ordinal patterns to classify the series, which allows us to search and compare by similarity on the stored data of the series represented. This classification strategy allows us to speed up the parameter search process, reduce the computational cost of the adjustment process and consequently improve energy cost savings. The experiences showed a reduction in the computational cost of 29% compared with our tuning methodology proposed in previous research.
description Fil: Trigila, Mariano. Pontificia Universidad Católica Argentina. Facultad de Ingeniería y Ciencias Agrarias; Argentina
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_3248
info:ar-repo/semantics/parteDeLibro
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv https://repositorio.uca.edu.ar/handle/123456789/17072
978-3-031-36023-7 (impreso)
978-3-031-36024-4 (online)
10.1007/978-3-031-36024-4_49
Trigila, M. et al. Reduction of the computational cost of tuning methodology of a simulator of a physical system [en línea]. En: Mikyška, J. et al. (eds). Computational Science. Lecture notes in Computer Science, vol 10475. Cham : Springer, 2023. doi: 10.1007/978-3-031-36024-4_49. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/17072
url https://repositorio.uca.edu.ar/handle/123456789/17072
identifier_str_mv 978-3-031-36023-7 (impreso)
978-3-031-36024-4 (online)
10.1007/978-3-031-36024-4_49
Trigila, M. et al. Reduction of the computational cost of tuning methodology of a simulator of a physical system [en línea]. En: Mikyška, J. et al. (eds). Computational Science. Lecture notes in Computer Science, vol 10475. Cham : Springer, 2023. doi: 10.1007/978-3-031-36024-4_49. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/17072
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv En: Mikyška, J. et al. (eds). Computational Science. Lecture notes in Computer Science, vol 10475. Cham : Springer, 2023.
reponame:Repositorio Institucional (UCA)
instname:Pontificia Universidad Católica Argentina
reponame_str Repositorio Institucional (UCA)
collection Repositorio Institucional (UCA)
instname_str Pontificia Universidad Católica Argentina
repository.name.fl_str_mv Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentina
repository.mail.fl_str_mv claudia_fernandez@uca.edu.ar
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score 13.070432

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