MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention
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Abstract (arXiv)
With the development of computational fluid dynamics, the requirements for the fluid simulation accuracy in industrial applications have also increased. The quality of the generated mesh directly affects the simulation accuracy. However, previous mesh quality metrics and models cannot evaluate meshes comprehensively and objectively. To this end, we propose MQENet, a structured mesh quality evaluation neural network based on dynamic graph attention. MQENet treats the mesh evaluation task as a graph classification task for classifying the quality of the input structured mesh. To make graphs generated from structured meshes more informative, MQENet introduces two novel structured mesh preprocessing algorithms. These two algorithms can also improve the conversion efficiency of structured mesh data. Experimental results on the benchmark structured mesh dataset NACA-Market show the effectiveness of MQENet in the mesh quality evaluation task.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 3 Sep 2023.
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21 of 43 references.
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9 more
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