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Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction

hal.structure.identifierESI Group [ESI Group]
dc.contributor.authorIBAÑEZ, R.
hal.structure.identifierInstitut de Calcul Intensif [ICI]
hal.structure.identifierESI Group [ESI Group]
dc.contributor.authorABISSET, Emmanuelle
hal.structure.identifierUniversidad de Zaragoza = University of Zaragoza [Saragossa University] = Université de Saragosse
dc.contributor.authorCUETO, Elías G.
hal.structure.identifierLaboratoire Angevin de Mécanique, Procédés et InnovAtion [LAMPA]
dc.contributor.authorAMMAR, Amine
hal.structure.identifierESI Group [ESI Group]
dc.contributor.authorDUVAL, Jean Louis
hal.structure.identifierLaboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
dc.contributor.authorCHINESTA, Francisco
dc.date.accessioned2021-05-14T09:37:42Z
dc.date.available2021-05-14T09:37:42Z
dc.date.issued2019
dc.identifier.issn0178-7675
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/76368
dc.description.abstractEnCompressed sensing is a signal compression technique with very remarkable properties. Among them, maybe the most salient one is its ability of overcoming the Shannon–Nyquist sampling theorem. In other words, it is able to reconstruct a signal at less than 2Q samplings per second, where Q stands for the highest frequency content of the signal. This property has, however, important applications in the field of computational mechanics, as we analyze in this paper. We consider a wide variety of applications, such as model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction. Examples are provided for all of them that show the potentialities of compressed sensing in terms of CPU savings in the field of computational mechanics.
dc.language.isoen
dc.publisherSpringer Verlag
dc.title.enSome applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction
dc.typeArticle de revue
dc.identifier.doi10.1007/s00466-019-01703-5
dc.subject.halSciences de l'ingénieur [physics]/Matériaux
bordeaux.journalComputational Mechanics
bordeaux.page1259-1271
bordeaux.volume64
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.issue5
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
bordeaux.peerReviewedoui
hal.identifierhal-02410086
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02410086v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computational%20Mechanics&rft.date=2019&rft.volume=64&rft.issue=5&rft.spage=1259-1271&rft.epage=1259-1271&rft.eissn=0178-7675&rft.issn=0178-7675&rft.au=IBA%C3%91EZ,%20R.&ABISSET,%20Emmanuelle&CUETO,%20El%C3%ADas%20G.&AMMAR,%20Amine&DUVAL,%20Jean%20Louis&rft.genre=article


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