A data-driven approach for the real-time modeling of spot-welded patches under impact using Deep Learning - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

A data-driven approach for the real-time modeling of spot-welded patches under impact using Deep Learning

Résumé

Solving large structural problems with complex localized behaviour is extremely challenging. To address this difficulty, both intrusive as well as non-intrusive multiscale methods have been developed in the past. To reduce the computational time further, we propose the use of Model Order Reduction (MOR) of local scale. This paper presents the MOR technique based on a novel physics-guided architecture (PGA) of neural networks, incorporating physical variables into the architecture. The proposed approach is illustrated in the case of spot-welded plates undergoing large deformation.
Fichier principal
Vignette du fichier
CFM_2022_article (2).pdf (2.32 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03825830 , version 1 (23-10-2022)

Identifiants

  • HAL Id : hal-03825830 , version 1

Citer

Afsal Pulikkathodi, Elisabeth Longatte-Lacazedieu, Ludovic Chamoin, Juan-Pedro Berro Ramirez, Laurent Rota, et al.. A data-driven approach for the real-time modeling of spot-welded patches under impact using Deep Learning. 25éme congrès français de mécanique, Aug 2022, Nantes, France. ⟨hal-03825830⟩
51 Consultations
27 Téléchargements

Partager

Gmail Facebook X LinkedIn More