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

Article dans une revue avec comité de lecture
Author
ccCUETO, Elias
95355 Universidad de Zaragoza = University of Zaragoza [Saragossa University] = Université de Saragosse
DUVAL, Jean-Louis
IBAÑEZ, Ruben
564849 ESI Group [ESI Group]
ccABISSET-CHAVANNE, Emmanuelle
445111 Institut de Calcul Intensif [ICI]
ccAMMAR, Amine
211916 Laboratoire Angevin de Mécanique, Procédés et InnovAtion [LAMPA]
ccCHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/17616
DOI
10.1007/s00466-019-01703-5
Date
2019
Journal
Computational Mechanics

Abstract

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.

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