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arXiv · 2211.09373

Graph Neural Network-based Surrogate Models for Finite Element Analysis

Meduri Venkata Shivaditya·José Alves·Francesca Bugiotti·Frederic Magoules
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Abstract

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 Network-based 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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Meduri Venkata Shivaditya, José Alves, Francesca Bugiotti, Frederic Magoules. 2022-11-17. Graph Neural Network-based Surrogate Models for Finite Element Analysis. https://arxiv.org/abs/2211.09373

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