A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

Xiaoxin Lu; Dimitris Giovanis; Julien Yvonnet; Vissarion Papadopoulos; Fabrice Detrez; Jinbo Bai

doi:10.1007/s00466-018-1643-0

Journal articles

A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

Xiaoxin Lu ¹ Dimitris Giovanis Julien Yvonnet ^{2, *} Vissarion Papadopoulos Fabrice Detrez Jinbo Bai ¹

* Corresponding author

1 MSSMat - Laboratoire de mécanique des sols, structures et matériaux

2 UPEM - Université Paris-Est Marne-la-Vallée

Document type :

Journal articles

Domain :

Physics [physics] / Mechanics [physics]

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https://hal-upec-upem.archives-ouvertes.fr/hal-02265344
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Submitted on : Friday, August 9, 2019 - 2:13:01 PM
Last modification on : Monday, July 18, 2022 - 1:24:39 PM

A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

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A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

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