Comptes Rendus
Data-driven computation for history-dependent materials
Comptes Rendus. Mécanique, Volume 347 (2019) no. 11, pp. 831-844.

This paper introduces a new vision of data-driven structure computation taking advantage of Material Science, especially for highly nonlinear and time-dependent material behaviours. Technical solutions are also derived, in order to build internal hidden variables defining the so-called “Experimental Constitutive Manifold”.

Received:
Accepted:
Published online:
DOI: 10.1016/j.crme.2019.11.008
Keywords: Data-driven, History-dependent materials, Computational mechanics, Big data, Experimental constitutive manifold, Material mechanics

Pierre Ladevèze 1; David Néron 1; Paul-William Gerbaud 1

1 LMT (ENS Paris-Saclay, CNRS, Université Paris-Saclay), 61, av. du Président-Wilson, 94235 Cachan, France
@article{CRMECA_2019__347_11_831_0,
     author = {Pierre Ladev\`eze and David N\'eron and Paul-William Gerbaud},
     title = {Data-driven computation for history-dependent materials},
     journal = {Comptes Rendus. M\'ecanique},
     pages = {831--844},
     publisher = {Elsevier},
     volume = {347},
     number = {11},
     year = {2019},
     doi = {10.1016/j.crme.2019.11.008},
     language = {en},
}
TY  - JOUR
AU  - Pierre Ladevèze
AU  - David Néron
AU  - Paul-William Gerbaud
TI  - Data-driven computation for history-dependent materials
JO  - Comptes Rendus. Mécanique
PY  - 2019
SP  - 831
EP  - 844
VL  - 347
IS  - 11
PB  - Elsevier
DO  - 10.1016/j.crme.2019.11.008
LA  - en
ID  - CRMECA_2019__347_11_831_0
ER  - 
%0 Journal Article
%A Pierre Ladevèze
%A David Néron
%A Paul-William Gerbaud
%T Data-driven computation for history-dependent materials
%J Comptes Rendus. Mécanique
%D 2019
%P 831-844
%V 347
%N 11
%I Elsevier
%R 10.1016/j.crme.2019.11.008
%G en
%F CRMECA_2019__347_11_831_0
Pierre Ladevèze; David Néron; Paul-William Gerbaud. Data-driven computation for history-dependent materials. Comptes Rendus. Mécanique, Volume 347 (2019) no. 11, pp. 831-844. doi : 10.1016/j.crme.2019.11.008. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2019.11.008/

[1] T. Kirchdoerfer; M. Ortiz Data-driven computational mechanics, Comput. Methods Appl. Mech. Eng., Volume 304 (2016), pp. 81-101 | arXiv | DOI

[2] T. Kirchdoerfer; M. Ortiz Data Driven Computing with noisy material data sets, Comput. Methods Appl. Mech. Eng., Volume 326 (2017), pp. 622-641 | arXiv | DOI

[3] T. Kirchdoerfer; M. Ortiz Data-driven computing in dynamics, Int. J. Numer. Methods Eng., Volume 113 (2018), pp. 1697-1710 | arXiv | DOI

[4] F. Chinesta; P. Ladeveze; R. Ibanez; J.V. Aguado; E. Abisset-Chavanne; E. Cueto (Procedia Engineering), Volume vol. 207, Elsevier B.V. (2017), pp. 209-214 | DOI

[5] R. Ibañez; D. Borzacchiello; J.V. Aguado; E. Cueto; P. Ladevèze; F. Chinesta; R. Ibañez; D. Borzacchiello; J.V. Aguado; E. Abisset-chavanne; R. Ibañez; D. Borzacchiello; J. Vicente; E.A-c. Elias; C. Pierre; F. Chinesta Data-driven non-linear elasticity: constitutive manifold construction and problem discretization, Comput. Mech. (2017)

[6] E. Lopez; D. Gonzalez; J.V. Aguado; E. Abisset-Chavanne; E. Cueto; C. Binetruy; F. Chinesta A manifold learning approach for integrated computational materials engineering, Arch. Comput. Methods Eng., Volume 25 (2018), pp. 59-68 | DOI

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

[8] M. Guo; J.S. Hesthaven Data-driven reduced order modeling for time-dependent problems, Comput. Methods Appl. Mech. Eng., Volume 345 (2019), pp. 75-99 | DOI

[9] A. Leygue; M. Coret; J. Réthoré; L. Stainier; E. Verron Data-based derivation of material response, Comput. Methods Appl. Mech. Eng., Volume 331 (2018), pp. 184-196 | DOI

[10] Z. Liu; M.A. Bessa; W.K. Liu Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials, Comput. Methods Appl. Mech. Eng., Volume 306 (2016), pp. 319-341 | DOI

[11] D. Versino; A. Tonda; C.A. Bronkhorst Data driven modeling of plastic deformation, Comput. Methods Appl. Mech. Eng., Volume 318 (2017), pp. 981-1004 | DOI

[12] P. Ladevèze Data Driven Structure Computation, 2018 (Technical Report, LMT)

[13] P. Ladevèze; F. Chinesta On the Concept of Constitutive Experimental Manifold in Nonlinear Mechanics, 2017 (Technical Report, LMT)

[14] P. Ladevèze; A. Chouaki Application of a posteriori error estimation for structural model updating, Inverse Probl., Volume 15 (1999), pp. 49-58 | DOI

[15] P. Ladevèze On reduced models in nonlinear solid mechanics, Eur. J. Mech. A, Solids, Volume 60 (2016), pp. 227-237 | DOI

[16] P. Ladevèze The large time increment method for the analysis of structures with non linear constitutive relation described by internal variables, C. R. Acad. Sci. Paris, Ser. IIb, Volume 309 (1989), pp. 1095-1099

[17] P. Ladevèze Sur une famille d'algorithmes en mécanique des structures, C. R. Acad. Sci. Paris, Ser. IIb, Volume 300 (1985), pp. 40-44

[18] P. Ladevèze; J.-P. Pelle Mastering Calculations in Linear and Nonlinear Mechanics, Springer, New York, 2005 https://doi-org.ezproxy.universite-paris-saclay.fr/10.1007/b138705

[19] P. Ladeveze; D. Leguillon Error estimate procedure in the finite element method and applications, SIAM J. Numer. Anal., Volume 20 (1983), pp. 485-509

[20] P. Ladevèze Nonlinear Computational Structural Mechanics—New Approaches and Non-Incremental Methods of Calculation, Springer Verlag, 1999

[21] F. Hild; A. Bouterf; L. Chamoin; H. Leclerc; F. Mathieu; J. Neggers; F. Pled; Z. Tomičević; S. Roux Toward 4D mechanical correlation, Adv. Model. Simul. Eng. Sci., Volume 3 (2016) | DOI

Cited by Sources:

Comments - Policy