An Alternative to Monte Carlo Simulation Method

Autores
Ballaben, Jorge Sebastian; Goicoechea, Hector Eduardo; Rosales, Marta Beatriz
Año de publicación
2018
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The quantification and propagation of uncertainty is a growing discipline, with applications within practically all sciences. Uncertainties are present in every prediction model of each discipline (natural, structural, biological, etc), since an exact and perfect definition of geometry, boundary conditions, material properties, initial conditions and excitations (among others) is rarely possible. A common and robust approach to perform the propagation of uncertainties is the Monte Carlo method, which usually implies running a large number of simulations. Complex systems, where uncertainty propagation is particularly interesting, require time expensive computations, and large memory and storage capacities in order to process such amount of data. Even thousands of runs of a slightly non-linear model with a few degrees of freedom could take a considerable time, despite the use of state-of-the-art solvers and parallelization techniques. In this work, a methodology that could allow the reduction of the number of simulations is discussed. The idea of the method is to perform a parametric sweep for a certain parameter X to be considered stochastic, then assign probabilities (according to a previously selected cumulative probability density function) to the values of X, and finally map the corresponding probability values to the target variables. Hence, the probability density function of the target variables could be estimated. Within this work, the theory and implementation of the proposed method are discussed and application examples are provided.
Fil: Ballaben, Jorge Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina
Fil: Goicoechea, Hector Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Física del Sur. Universidad Nacional del Sur. Departamento de Física. Instituto de Física del Sur; Argentina
Fil: Rosales, Marta Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Física del Sur. Universidad Nacional del Sur. Departamento de Física. Instituto de Física del Sur; Argentina
XII Congreso Argentino de Mecánica Computacional
San Miguel de Tucumán
Argentina
Asociación Argentina de Mecánica Computacional
Materia
UNCERTAINTY PROPAGATION
MONTE CARLO ALTERNATIVE
PARAMETRIC SWEEP REUTILIZATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/188863
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling An Alternative to Monte Carlo Simulation MethodBallaben, Jorge SebastianGoicoechea, Hector EduardoRosales, Marta BeatrizUNCERTAINTY PROPAGATIONMONTE CARLO ALTERNATIVEPARAMETRIC SWEEP REUTILIZATIONhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1The quantification and propagation of uncertainty is a growing discipline, with applications within practically all sciences. Uncertainties are present in every prediction model of each discipline (natural, structural, biological, etc), since an exact and perfect definition of geometry, boundary conditions, material properties, initial conditions and excitations (among others) is rarely possible. A common and robust approach to perform the propagation of uncertainties is the Monte Carlo method, which usually implies running a large number of simulations. Complex systems, where uncertainty propagation is particularly interesting, require time expensive computations, and large memory and storage capacities in order to process such amount of data. Even thousands of runs of a slightly non-linear model with a few degrees of freedom could take a considerable time, despite the use of state-of-the-art solvers and parallelization techniques. In this work, a methodology that could allow the reduction of the number of simulations is discussed. The idea of the method is to perform a parametric sweep for a certain parameter X to be considered stochastic, then assign probabilities (according to a previously selected cumulative probability density function) to the values of X, and finally map the corresponding probability values to the target variables. Hence, the probability density function of the target variables could be estimated. Within this work, the theory and implementation of the proposed method are discussed and application examples are provided.Fil: Ballaben, Jorge Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; ArgentinaFil: Goicoechea, Hector Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Física del Sur. Universidad Nacional del Sur. Departamento de Física. Instituto de Física del Sur; ArgentinaFil: Rosales, Marta Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Física del Sur. Universidad Nacional del Sur. Departamento de Física. Instituto de Física del Sur; ArgentinaXII Congreso Argentino de Mecánica ComputacionalSan Miguel de TucumánArgentinaAsociación Argentina de Mecánica ComputacionalAsociación Argentina de Mecánica ComputacionalEtse, José G.Luccioni, Bibiana MariaPucheta, Martín AlejoStorti, Mario Alberto2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/188863An Alternative to Monte Carlo Simulation Method; XII Congreso Argentino de Mecánica Computacional; San Miguel de Tucumán; Argentina; 2018; 631-6402591-3522CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://cimec.org.ar/ojs/index.php/mc/article/view/5563/5540Nacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-04-15T10:06:44Zoai:ri.conicet.gov.ar:11336/188863instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-04-15 10:06:45.174CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An Alternative to Monte Carlo Simulation Method
title An Alternative to Monte Carlo Simulation Method
spellingShingle An Alternative to Monte Carlo Simulation Method
Ballaben, Jorge Sebastian
UNCERTAINTY PROPAGATION
MONTE CARLO ALTERNATIVE
PARAMETRIC SWEEP REUTILIZATION
title_short An Alternative to Monte Carlo Simulation Method
title_full An Alternative to Monte Carlo Simulation Method
title_fullStr An Alternative to Monte Carlo Simulation Method
title_full_unstemmed An Alternative to Monte Carlo Simulation Method
title_sort An Alternative to Monte Carlo Simulation Method
dc.creator.none.fl_str_mv Ballaben, Jorge Sebastian
Goicoechea, Hector Eduardo
Rosales, Marta Beatriz
author Ballaben, Jorge Sebastian
author_facet Ballaben, Jorge Sebastian
Goicoechea, Hector Eduardo
Rosales, Marta Beatriz
author_role author
author2 Goicoechea, Hector Eduardo
Rosales, Marta Beatriz
author2_role author
author
dc.contributor.none.fl_str_mv Etse, José G.
Luccioni, Bibiana Maria
Pucheta, Martín Alejo
Storti, Mario Alberto
dc.subject.none.fl_str_mv UNCERTAINTY PROPAGATION
MONTE CARLO ALTERNATIVE
PARAMETRIC SWEEP REUTILIZATION
topic UNCERTAINTY PROPAGATION
MONTE CARLO ALTERNATIVE
PARAMETRIC SWEEP REUTILIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The quantification and propagation of uncertainty is a growing discipline, with applications within practically all sciences. Uncertainties are present in every prediction model of each discipline (natural, structural, biological, etc), since an exact and perfect definition of geometry, boundary conditions, material properties, initial conditions and excitations (among others) is rarely possible. A common and robust approach to perform the propagation of uncertainties is the Monte Carlo method, which usually implies running a large number of simulations. Complex systems, where uncertainty propagation is particularly interesting, require time expensive computations, and large memory and storage capacities in order to process such amount of data. Even thousands of runs of a slightly non-linear model with a few degrees of freedom could take a considerable time, despite the use of state-of-the-art solvers and parallelization techniques. In this work, a methodology that could allow the reduction of the number of simulations is discussed. The idea of the method is to perform a parametric sweep for a certain parameter X to be considered stochastic, then assign probabilities (according to a previously selected cumulative probability density function) to the values of X, and finally map the corresponding probability values to the target variables. Hence, the probability density function of the target variables could be estimated. Within this work, the theory and implementation of the proposed method are discussed and application examples are provided.
Fil: Ballaben, Jorge Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina
Fil: Goicoechea, Hector Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Física del Sur. Universidad Nacional del Sur. Departamento de Física. Instituto de Física del Sur; Argentina
Fil: Rosales, Marta Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Física del Sur. Universidad Nacional del Sur. Departamento de Física. Instituto de Física del Sur; Argentina
XII Congreso Argentino de Mecánica Computacional
San Miguel de Tucumán
Argentina
Asociación Argentina de Mecánica Computacional
description The quantification and propagation of uncertainty is a growing discipline, with applications within practically all sciences. Uncertainties are present in every prediction model of each discipline (natural, structural, biological, etc), since an exact and perfect definition of geometry, boundary conditions, material properties, initial conditions and excitations (among others) is rarely possible. A common and robust approach to perform the propagation of uncertainties is the Monte Carlo method, which usually implies running a large number of simulations. Complex systems, where uncertainty propagation is particularly interesting, require time expensive computations, and large memory and storage capacities in order to process such amount of data. Even thousands of runs of a slightly non-linear model with a few degrees of freedom could take a considerable time, despite the use of state-of-the-art solvers and parallelization techniques. In this work, a methodology that could allow the reduction of the number of simulations is discussed. The idea of the method is to perform a parametric sweep for a certain parameter X to be considered stochastic, then assign probabilities (according to a previously selected cumulative probability density function) to the values of X, and finally map the corresponding probability values to the target variables. Hence, the probability density function of the target variables could be estimated. Within this work, the theory and implementation of the proposed method are discussed and application examples are provided.
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/188863
An Alternative to Monte Carlo Simulation Method; XII Congreso Argentino de Mecánica Computacional; San Miguel de Tucumán; Argentina; 2018; 631-640
2591-3522
CONICET Digital
CONICET
url http://hdl.handle.net/11336/188863
identifier_str_mv An Alternative to Monte Carlo Simulation Method; XII Congreso Argentino de Mecánica Computacional; San Miguel de Tucumán; Argentina; 2018; 631-640
2591-3522
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://cimec.org.ar/ojs/index.php/mc/article/view/5563/5540
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Nacional
dc.publisher.none.fl_str_mv Asociación Argentina de Mecánica Computacional
publisher.none.fl_str_mv Asociación Argentina de Mecánica Computacional
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.203462

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