Numerical experiments on unsupervised manifold learning applied to mechanical modeling of materials and structures-Reference-Cited by-同舟云学术

Numerical experiments on unsupervised manifold learning applied to mechanical modeling of materials and structures

Published:2021-01-13 Issue:10-11 Volume:348 Page:937-958
ISSN:1873-7234
Container-title:Comptes Rendus. Mécanique
language:en
Short-container-title:

Author:

Ibanez Ruben,Gilormini Pierre,Cueto Elias,Chinesta Francisco

Publisher

Cellule MathDoc/CEDRAM

Subject

Mechanics of Materials,General Materials Science

Link

https://comptes-rendus.academie-sciences.fr/mecanique/article/CRMECA_2020__348_10-11_937_0.pdf

Reference16 articles.

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2. [2] Bessa, M. A.; Bostanabad, R.; Liu, Z.; Hu, A.; Apley, D. W.; Brinson, C.; Chen, W.; Liu, W. K. A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality, Comput. Methods Appl. Mech. Eng., Volume 320 (2017), pp. 633-667

3. [3] Liu, Z.; Fleming, M.; Liu, W. K. Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials, Comput. Methods Appl. Mech. Eng., Volume 330 (2018), pp. 547-577

4. [4] Gonzalez, D.; Chinesta, F.; Cueto, E. Thermodynamically consistent data-driven computational mechanics, Contin. Mech. Thermodyn., Volume 31 (2019), pp. 239-253

5. [5] Ibanez, R.; Abisset-Chavanne, E.; Aguado, J.V.; Gonzalez, D.; Cueto, E.; Chinesta, F. A manifold learning approach to data-driven computational elasticity and inelasticity, Arch. Comput. Methods Eng., Volume 25 (2018) no. 1, pp. 47-57

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