Communication Dans Un Congrès Année : 2024

Training an AI hyperelastic constitutive model with experimental data

Résumé

A Physics-Augmented Neural network is trained to model a hyperelastic behavior. The dataset used for the training, validation, and test are displacement-force couples obtained from two experiments on a rubber-like material. One experiment was dedicated for the test, to assess the capacity of the model to generalize on unseen loadings and geometries. The trained AI model outperforms a standard Neo Hookean model identified on the same data. Particular attention is paid to the mechanical data information contained in the different datasets.

Fichier principal
Vignette du fichier
main.pdf (1.72 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04720898 , version 1 (04-10-2024)

Licence

Identifiants

Citer

Clément Jailin, Antoine Benady, Emmanuel Baranger. Training an AI hyperelastic constitutive model with experimental data. PhotoMechanics - iDICs 2024, Oct 2024, Clermont - Ferrand, France. ⟨hal-04720898⟩
1354 Consultations
308 Téléchargements

Altmetric

Partager

  • More