A Hybrid GNN approach for predicting node data for 3D meshes
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Abstract (arXiv)
Metal forging is used to manufacture dies. We require the best set of input parameters for the process to be efficient. Currently, we predict the best parameters using the finite element method by generating simulations for the different initial conditions, which is a time-consuming process. In this paper, introduce a hybrid approach that helps in processing and generating new data simulations using a surrogate graph neural network model based on graph convolutions, having a cheaper time cost. We also introduce a hybrid approach that helps in processing and generating new data simulations using the model. Given a dataset representing meshes, our focus is on the conversion of the available information into a graph or point cloud structure. This new representation enables deep learning. The predicted result is similar, with a low error when compared to that produced using the finite element method. The new models have outperformed existing PointNet and simple graph neural network models when applied to produce the simulations.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 23 Oct 2023.
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References · 8
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Inductive Representation Learning on Large Graphscited by 6,320
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
- 2016An overview of gradient descent optimization algorithmscited by 1,033
- 2018MeshNet: Mesh Neural Network for 3D Shape Representationcited by 95
- 2022Graph Neural Network-based Surrogate Models for Finite Element Analysiscited by 7
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