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Computational Mechanics

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10.04.2019 | Original Paper

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

verfasst von: R. Ibañez, E. Abisset-Chavanne, E. Cueto, A. Ammar, J. -L. Duval, F. Chinesta

Erschienen in: Computational Mechanics | Ausgabe 5/2019

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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.

Vorheriger Artikel Reduction process based on proper orthogonal decomposition for dual formulation of dynamic substructures

Nächster Artikel Isotropic hyperelasticity in principal stretches: explicit elasticity tensors and numerical implementation

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Titel: Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction
verfasst von: R. Ibañez
E. Abisset-Chavanne
E. Cueto
A. Ammar
J. -L. Duval
F. Chinesta
Publikationsdatum: 10.04.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Computational Mechanics / Ausgabe 5/2019
Print ISSN: 0178-7675
Elektronische ISSN: 1432-0924
DOI: https://doi.org/10.1007/s00466-019-01703-5

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