Adaptive empirical distributions in the framework of inverse problems

Detalhes bibliográficos
Autor(a) principal: Silva, Tiago
Data de Publicação: 2017
Outros Autores: Loja, Amélia, Carvalho, Alda, Maia, Nuno. M., Barbosa, Joaquim
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.21/8068
Resumo: This article presents an innovative framework regarding an inverse problem. One presents the extension of a global optimization algorithm to estimate not only an optimal set of modeling parameters, but also their optimal distributions. Regarding its characteristics, differential evolution algorithm is used to demonstrate this extension, although other population-based algorithms may be considered. The adaptive empirical distributions algorithm is here introduced for the same purpose. Both schemes rely on the minimization of the dissimilarity between the empirical cumulative distribution functions of two data sets, using a goodness-of-fit test to evaluate their resemblance.
id RCAP_3c10e25098495f0ddaffe23859a3deea
oai_identifier_str oai:repositorio.ipl.pt:10400.21/8068
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Adaptive empirical distributions in the framework of inverse problemsAdaptive empirical distributionsDifferential evolutionEmpirical CDFInverse problemInverse samplingTwo samples Kolmogorov-Smirnov goodness-of-fit testThis article presents an innovative framework regarding an inverse problem. One presents the extension of a global optimization algorithm to estimate not only an optimal set of modeling parameters, but also their optimal distributions. Regarding its characteristics, differential evolution algorithm is used to demonstrate this extension, although other population-based algorithms may be considered. The adaptive empirical distributions algorithm is here introduced for the same purpose. Both schemes rely on the minimization of the dissimilarity between the empirical cumulative distribution functions of two data sets, using a goodness-of-fit test to evaluate their resemblance.Taylor & FrancisRCIPLSilva, TiagoLoja, AméliaCarvalho, AldaMaia, Nuno. M.Barbosa, Joaquim2018-02-20T09:43:48Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/8068engSILVA, Tiago A. N.; [et al] – Adaptive empirical distributions in the framework of inverse problems. International Journal for Computational Methods in Engineering Science & Mechanics. ISSN 1550-2287. Vol. 18, N.º 6 (2017), pp. 277-2911550-228710.1080/15502287.2017.1287227metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-03T09:54:59Zoai:repositorio.ipl.pt:10400.21/8068Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:16:54.247857Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Adaptive empirical distributions in the framework of inverse problems
title Adaptive empirical distributions in the framework of inverse problems
spellingShingle Adaptive empirical distributions in the framework of inverse problems
Silva, Tiago
Adaptive empirical distributions
Differential evolution
Empirical CDF
Inverse problem
Inverse sampling
Two samples Kolmogorov-Smirnov goodness-of-fit test
title_short Adaptive empirical distributions in the framework of inverse problems
title_full Adaptive empirical distributions in the framework of inverse problems
title_fullStr Adaptive empirical distributions in the framework of inverse problems
title_full_unstemmed Adaptive empirical distributions in the framework of inverse problems
title_sort Adaptive empirical distributions in the framework of inverse problems
author Silva, Tiago
author_facet Silva, Tiago
Loja, Amélia
Carvalho, Alda
Maia, Nuno. M.
Barbosa, Joaquim
author_role author
author2 Loja, Amélia
Carvalho, Alda
Maia, Nuno. M.
Barbosa, Joaquim
author2_role author
author
author
author
dc.contributor.none.fl_str_mv RCIPL
dc.contributor.author.fl_str_mv Silva, Tiago
Loja, Amélia
Carvalho, Alda
Maia, Nuno. M.
Barbosa, Joaquim
dc.subject.por.fl_str_mv Adaptive empirical distributions
Differential evolution
Empirical CDF
Inverse problem
Inverse sampling
Two samples Kolmogorov-Smirnov goodness-of-fit test
topic Adaptive empirical distributions
Differential evolution
Empirical CDF
Inverse problem
Inverse sampling
Two samples Kolmogorov-Smirnov goodness-of-fit test
description This article presents an innovative framework regarding an inverse problem. One presents the extension of a global optimization algorithm to estimate not only an optimal set of modeling parameters, but also their optimal distributions. Regarding its characteristics, differential evolution algorithm is used to demonstrate this extension, although other population-based algorithms may be considered. The adaptive empirical distributions algorithm is here introduced for the same purpose. Both schemes rely on the minimization of the dissimilarity between the empirical cumulative distribution functions of two data sets, using a goodness-of-fit test to evaluate their resemblance.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01T00:00:00Z
2018-02-20T09:43:48Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.21/8068
url http://hdl.handle.net/10400.21/8068
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv SILVA, Tiago A. N.; [et al] – Adaptive empirical distributions in the framework of inverse problems. International Journal for Computational Methods in Engineering Science & Mechanics. ISSN 1550-2287. Vol. 18, N.º 6 (2017), pp. 277-291
1550-2287
10.1080/15502287.2017.1287227
dc.rights.driver.fl_str_mv metadata only access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv metadata only access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799133430611443712

Registros relacionados