Skip to Main content Skip to Navigation
Journal articles

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

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.
Complete list of metadatas

Cited literature [43 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02410086
Contributor : Compte de Service Administrateur Ensam <>
Submitted on : Friday, December 13, 2019 - 4:38:40 PM
Last modification on : Monday, March 30, 2020 - 8:45:51 AM

File

PIMM_CM_2019_IBANEZ.pdf
Files produced by the author(s)

Identifiers

Citation

R. Ibañez, Emmanuelle Abisset-Chavanne, Elías G. Cueto, Amine Ammar, Jean Louis Duval, et al.. Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction. Computational Mechanics, Springer Verlag, 2019, 64 (5), pp.1259-1271. ⟨10.1007/s00466-019-01703-5⟩. ⟨hal-02410086⟩

Share

Metrics

Record views

75

Files downloads

192