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Advanced separated spatial representations for hardly separable domains

Article dans une revue avec comité de lecture
Auteur
GHNATIOS, Chady
ccABISSET-CHAVANNE, Emmanuelle
1002421 Institut de Mécanique et d'Ingénierie [I2M]
ccAMMAR, Amine
211916 Laboratoire Angevin de Mécanique, Procédés et InnovAtion [LAMPA]
ccCUETO, Elias
ccDUVAL, Jean-Louis
ccCHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/15677
DOI
10.1016/j.cma.2019.05.047
Date
2019
Journal
Computer Methods in Applied Mechanics and Engineering

Résumé

This work aims at proposing a new procedure for parametric problems whose separated representation has been considered difficult, or whose SVD compression impacted the results in terms of performance and accuracy. The proposed technique achieves a fully separated representation for layered domains with interfaces exhibiting waviness or – more generally – deviating from planar surfaces, parallel to the coordinate plane. This will make possible a simple separated representation, equivalent to others, already analyzed in some of our former works. To prove the potentialities of the proposed approach, two benchmarks will be addressed, one of them involving an efficient space–time separated representation achieved by considering the same rationale.

Fichier(s) constituant cette publication

Nom:
PIMM_CMAM_2019_GHNATIOS.pdf
Taille:
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Format:
PDF
Fin d'embargo:
2020-01-01
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Cette publication figure dans le(s) laboratoire(s) suivant(s)

  • Institut de Mécanique et d’Ingénierie de Bordeaux (I2M)
  • Laboratoire Angevin de Mécanique, Procédés et InnovAtion (LAMPA)
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)

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Visualiser des documents liés par titre, auteur, créateur et sujet.

  • 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
    ccCUETO, Elias; DUVAL, Jean-Louis; IBAÑEZ, Ruben; ccABISSET-CHAVANNE, Emmanuelle; ccAMMAR, Amine; ccCHINESTA SORIA, Francisco (Springer Verlag, 2019)
    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 ...
  • A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition 
    Article dans une revue avec comité de lecture
    IBAÑEZ, Ruben; ccABISSET-CHAVANNE, Emmanuelle; ccAMMAR, Amine; GONZALEZ, David; ccCUETO, Elias; HUERTA, Antonio; DUVAL, Jean-Louis; ccCHINESTA SORIA, Francisco (Wiley, 2018)
    Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional ...
  • Advanced modeling and simulation of sheet moulding compound (SMC) processes 
    Article dans une revue avec comité de lecture
    PEREZ, Marta; PRONO, David; ccGHNATIOS, Chady; ccABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean-Louis; ccCHINESTA SORIA, Francisco (Springer Verlag, 2019)
    In SMC processes, a charge of a composite material, which typically consists of a matrix composed of an unsaturated polyester or vinylester, reinforced with chopped glass fibres or carbon fi bre bundles and fillers, is ...
  • On the effective conductivity and the apparent viscosity of a thin rough polymer interface using PGD‐based separated representations 
    Article dans une revue avec comité de lecture
    ccGHNATIOS, Chady; DELPLACE, Frank; BARASINSKI, Anais; DUVAL, Jean-Louis; ccCUETO, Elias; ccAMMAR, Amine; ccCHINESTA SORIA, Francisco (Wiley, 2020)
    Composite manufacturing processes usually proceed from preimpregnated preforms that are consolidated by simultaneously applying heat and pressure, so as to ensure a perfect contact compulsory for making molecular diffusion ...
  • Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit 
    Article dans une revue avec comité de lecture
    REILLE, Agathe; HASCOET, Nicolas; ccCUETO, Elias; DUVAL, Jean-Louis; KEUNINGS, Roland; ccGHNATIOS, Chady; ccAMMAR, Amine; ccCHINESTA SORIA, Francisco (Elsevier Masson, 2019)
    The present work aims at proposing a new methodology for learning reduced models from a small amount of data. It is based on the fact that discrete models, or their transfer function counterparts, have a low rank and then ...

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