The structure of reconstructed flows in latent spaces

Autores
Uribarri, Gonzalo; Mindlin, Bernardo Gabriel
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Reconstructing the flow of a dynamical system from experimental data has been a key tool in the study of nonlinear problems. It allows one to discover the equations ruling the dynamics of a system as well as to quantify its complexity. In this work, we study the topology of the flow reconstructed by autoencoders, a dimensionality reduction method based on deep neural networks that has recently proved to be a very powerful tool for this task. We show that, although in many cases proper embeddings can be obtained with this method, it is not always the case that the topological structure of the flow is preserved.
Fil: Uribarri, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Materia
neural networks
autoencoders
nonlinear dynamics
chaos
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:11336/146082
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling The structure of reconstructed flows in latent spacesUribarri, GonzaloMindlin, Bernardo Gabrielneural networksautoencodersnonlinear dynamicschaoshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Reconstructing the flow of a dynamical system from experimental data has been a key tool in the study of nonlinear problems. It allows one to discover the equations ruling the dynamics of a system as well as to quantify its complexity. In this work, we study the topology of the flow reconstructed by autoencoders, a dimensionality reduction method based on deep neural networks that has recently proved to be a very powerful tool for this task. We show that, although in many cases proper embeddings can be obtained with this method, it is not always the case that the topological structure of the flow is preserved.Fil: Uribarri, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaAmerican Institute of Physics2020-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/146082Uribarri, Gonzalo; Mindlin, Bernardo Gabriel; The structure of reconstructed flows in latent spaces; American Institute of Physics; Chaos; 30; 9; 9-2020; 1-91054-1500CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1063/5.0013714info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:43:15Zoai:ri.conicet.gov.ar:11336/146082instacron: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:34982025-10-15 15:43:16.227CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The structure of reconstructed flows in latent spaces
title The structure of reconstructed flows in latent spaces
spellingShingle The structure of reconstructed flows in latent spaces
Uribarri, Gonzalo
neural networks
autoencoders
nonlinear dynamics
chaos
title_short The structure of reconstructed flows in latent spaces
title_full The structure of reconstructed flows in latent spaces
title_fullStr The structure of reconstructed flows in latent spaces
title_full_unstemmed The structure of reconstructed flows in latent spaces
title_sort The structure of reconstructed flows in latent spaces
dc.creator.none.fl_str_mv Uribarri, Gonzalo
Mindlin, Bernardo Gabriel
author Uribarri, Gonzalo
author_facet Uribarri, Gonzalo
Mindlin, Bernardo Gabriel
author_role author
author2 Mindlin, Bernardo Gabriel
author2_role author
dc.subject.none.fl_str_mv neural networks
autoencoders
nonlinear dynamics
chaos
topic neural networks
autoencoders
nonlinear dynamics
chaos
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Reconstructing the flow of a dynamical system from experimental data has been a key tool in the study of nonlinear problems. It allows one to discover the equations ruling the dynamics of a system as well as to quantify its complexity. In this work, we study the topology of the flow reconstructed by autoencoders, a dimensionality reduction method based on deep neural networks that has recently proved to be a very powerful tool for this task. We show that, although in many cases proper embeddings can be obtained with this method, it is not always the case that the topological structure of the flow is preserved.
Fil: Uribarri, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Mindlin, Bernardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
description Reconstructing the flow of a dynamical system from experimental data has been a key tool in the study of nonlinear problems. It allows one to discover the equations ruling the dynamics of a system as well as to quantify its complexity. In this work, we study the topology of the flow reconstructed by autoencoders, a dimensionality reduction method based on deep neural networks that has recently proved to be a very powerful tool for this task. We show that, although in many cases proper embeddings can be obtained with this method, it is not always the case that the topological structure of the flow is preserved.
publishDate 2020
dc.date.none.fl_str_mv 2020-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/146082
Uribarri, Gonzalo; Mindlin, Bernardo Gabriel; The structure of reconstructed flows in latent spaces; American Institute of Physics; Chaos; 30; 9; 9-2020; 1-9
1054-1500
CONICET Digital
CONICET
url http://hdl.handle.net/11336/146082
identifier_str_mv Uribarri, Gonzalo; Mindlin, Bernardo Gabriel; The structure of reconstructed flows in latent spaces; American Institute of Physics; Chaos; 30; 9; 9-2020; 1-9
1054-1500
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1063/5.0013714
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
dc.publisher.none.fl_str_mv American Institute of Physics
publisher.none.fl_str_mv American Institute of Physics
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
_version_ 1846083538567299072
score 13.22299

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