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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300878


Proposing an intelligent mesh-smoothing method with graph neural networks


Author(s):  Zhichao WANG, Xinhai CHEN, Junjun YAN, Jie LIU

Affiliation(s):  Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410073, China; more

Corresponding email(s):   wangzhichao@nudt.edu.cn, chenxinhai16@nudt.edu.cn

Key Words:  Unstructured mesh, Mesh smoothing, Graph neural network, Optimization-based smoothing


Zhichao WANG, Xinhai CHEN, Junjun YAN, Jie LIU. Proposing an intelligent mesh-smoothing method with graph neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
In computational fluid dynamics (CFD), mesh-smoothing methods are commonly utilized to refine the mesh quality for achieving high-precision numerical simulations. Specifically, optimization-based smoothing is used for high-quality mesh smoothing, but it incurs significant computational overhead. Pioneer works improve its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality meshes. However, they pose difficulty in smoothing the mesh nodes with varying degrees and also require data augmentation to address the node input sequence problem. Additionally, the required labeled high-quality meshes further limit the applicability of the proposed method. In the present paper, we present GMSNet, a lightweight neural network model for intelligent mesh smoothing. GMSNet adopts graph neural networks (GNNs) to extract features of the node’s neighbors and outputs the optimal node position. During smoothing, we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume elements. With a lightweight model, GMSNet can effectively smooth mesh nodes with varying degrees and remain unaffected by the order of input data. A novel loss function, MetricLoss, is also developed to eliminate the need for high-quality meshes, which provides a stable and rapid convergence during training. We compare GMSNet with commonly used mesh-smoothing methods on two-dimensional triangle meshes. The experimental results show that the GMSNet achieves outstanding meshsmoothing performances with 5% model parameters of the previous model, and attains 13.56 times faster than optimization-based smoothing.

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