Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction-Reference-Cited by-同舟云学术

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

Published:2019-04-10 Issue:5 Volume:64 Page:1259-1271
ISSN:0178-7675
Container-title:Computational Mechanics
language:en
Short-container-title:Comput Mech

Author:

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

Funder

Ministerio de Ciencia e Innovación

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Computational Theory and Mathematics,Mechanical Engineering,Ocean Engineering,Computational Mechanics

Link

http://link.springer.com/content/pdf/10.1007/s00466-019-01703-5.pdf

Reference43 articles.

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2. Ammar A, Mokdad B, Chinesta F, Keunings R (2007) A new family of solvers for some classes of multidimensional partial differential equations encountered in kinetic theory modeling of complex fluids. Part II: transient simulation using space-time separated representations. J Non-Newton Fluid Mech 144:98–121

3. Amsallem D, Farhat C (2008) An interpolation method for adapting reduced-order models and application to aeroelasticity. AIAA J 46:1803–1813

4. Benner P, Gugercin S, Willcox K (2015) A survey of projection-based model reduction methods for parametric dynamical systems. SIAM Rev 57(4):483–531

5. Bognet B, Bordeu F, Chinesta F, Leygue A, Poitou A (2012) Advanced simulation of models defined in plate geometries: 3D solutions with 2D computational complexity. Comput Methods Appl Mech Eng 201–204:1–12. https://doi.org/10.1016/j.cma.2011.08.025

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