Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction
| hal.structure.identifier | ESI Group [ESI Group] | |
| dc.contributor.author | IBAÑEZ, R. | |
| hal.structure.identifier | Institut de Calcul Intensif [ICI] | |
| hal.structure.identifier | ESI Group [ESI Group] | |
| dc.contributor.author | ABISSET, Emmanuelle | |
| hal.structure.identifier | University of Zaragoza - Universidad de Zaragoza [Zaragoza] | |
| dc.contributor.author | CUETO, Elías G. | |
| hal.structure.identifier | Laboratoire Angevin de Mécanique, Procédés et InnovAtion [LAMPA] | |
| dc.contributor.author | AMMAR, Amine | |
| hal.structure.identifier | ESI Group [ESI Group] | |
| dc.contributor.author | DUVAL, Jean Louis | |
| hal.structure.identifier | Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM] | |
| dc.contributor.author | CHINESTA, Francisco | |
| dc.date.accessioned | 2021-05-14T09:37:42Z | |
| dc.date.available | 2021-05-14T09:37:42Z | |
| dc.date.issued | 2019 | |
| dc.identifier.issn | 0178-7675 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/76368 | |
| dc.description.abstractEn | Compressed 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.iso | en | |
| dc.publisher | Springer Verlag | |
| dc.title.en | Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1007/s00466-019-01703-5 | |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Matériaux | |
| bordeaux.journal | Computational Mechanics | |
| bordeaux.page | 1259-1271 | |
| bordeaux.volume | 64 | |
| bordeaux.hal.laboratories | Institut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295 | * |
| bordeaux.issue | 5 | |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.institution | INRAE | |
| bordeaux.institution | Arts et Métiers | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-02410086 | |
| hal.version | 1 | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-02410086v1 | |
| bordeaux.COinS | ctx_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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