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

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 si...

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Detalles Bibliográficos
Autores principales: Trigila, Mariano, Gaudiani, Adriana, Wong, Alvaro, Rexachs, Dolores, Luque, Emilio
Formato: Parte de libro
Lenguaje:Inglés
Publicado: Springer 2023
Materias:
SIMULACION PARAMETRICA
METODOLOGIA DE SINTONIZACION
PATRON ORDINAL
BASES DE DATOS
HERRAMIENTAS INFORMATICAS
SOFTWARE
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/17072
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139-123456789-17072
record_format dspace
spelling I33-R139-123456789-170722023-09-08T05:01:12Z Reduction of the computational cost of tuning methodology of a simulator of a physical system Trigila, Mariano Gaudiani, Adriana Wong, Alvaro Rexachs, Dolores Luque, Emilio SIMULACION PARAMETRICA METODOLOGIA DE SINTONIZACION PATRON ORDINAL BASES DE DATOS HERRAMIENTAS INFORMATICAS SOFTWARE 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. 2023-09-07T10:45:57Z 2023-09-07T10:45:57Z 2023 Parte de libro 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 978-3-031-36023-7 (impreso) 978-3-031-36024-4 (online) https://repositorio.uca.edu.ar/handle/123456789/17072 10.1007/978-3-031-36024-4_49 eng Acceso restringido http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer En: Mikyška, J. et al. (eds). Computational Science. Lecture notes in Computer Science, vol 10475. Cham : Springer, 2023.
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic SIMULACION PARAMETRICA
METODOLOGIA DE SINTONIZACION
PATRON ORDINAL
BASES DE DATOS
HERRAMIENTAS INFORMATICAS
SOFTWARE
spellingShingle SIMULACION PARAMETRICA
METODOLOGIA DE SINTONIZACION
PATRON ORDINAL
BASES DE DATOS
HERRAMIENTAS INFORMATICAS
SOFTWARE
Trigila, Mariano
Gaudiani, Adriana
Wong, Alvaro
Rexachs, Dolores
Luque, Emilio
Reduction of the computational cost of tuning methodology of a simulator of a physical system
topic_facet SIMULACION PARAMETRICA
METODOLOGIA DE SINTONIZACION
PATRON ORDINAL
BASES DE DATOS
HERRAMIENTAS INFORMATICAS
SOFTWARE
description 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.
format Parte de libro
author Trigila, Mariano
Gaudiani, Adriana
Wong, Alvaro
Rexachs, Dolores
Luque, Emilio
author_facet Trigila, Mariano
Gaudiani, Adriana
Wong, Alvaro
Rexachs, Dolores
Luque, Emilio
author_sort Trigila, Mariano
title Reduction of the computational cost of tuning methodology of a simulator of a physical system
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
publisher Springer
publishDate 2023
url https://repositorio.uca.edu.ar/handle/123456789/17072
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AT wongalvaro reductionofthecomputationalcostoftuningmethodologyofasimulatorofaphysicalsystem
AT rexachsdolores reductionofthecomputationalcostoftuningmethodologyofasimulatorofaphysicalsystem
AT luqueemilio reductionofthecomputationalcostoftuningmethodologyofasimulatorofaphysicalsystem
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