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Communication Dans Un Congrès Année : 2022

Graph Neural Network-based Surrogate Models for Finite Element Analysis

Résumé

Current simulation of metal forging processes use advanced finite element methods. Such methods consist of solving mathematical equations, which takes a significant amount of time for the simulation to complete. Computational time can be prohibitive for parametric response surface exploration tasks. In this paper, we propose as an alternative, a Graph Neural Networkbased graph prediction model to act as a surrogate model for parameters search space exploration and which exhibits a time cost reduced by an order of magnitude. Numerical experiments show that this new model outperforms the Point-Net model and the Dynamic Graph Convolutional Neural Net model.
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Dates et versions

hal-04142064 , version 1 (26-06-2023)

Identifiants

  • HAL Id : hal-04142064 , version 1

Citer

Venkata Meduri, José Alves, Francesca Bugiotti, Frederic Magoules. Graph Neural Network-based Surrogate Models for Finite Element Analysis. 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Oct 2022, Chizhou, China. ⟨hal-04142064⟩
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