MTGNet: multi-label mesh quality evaluation using topology-guided graph neural network

Mesh quality directly affects the accuracy and efficiency of numerical simulation. Mesh quality evaluation aims to evaluate the suitability of the mesh generated in CAE pre-processing for numerical simulation. Recent work has introduced deep neural networks for mesh quality evaluation. However, these methods treat the mesh quality evaluation task as a multi-classification problem, resulting in serious correlations among different quality categories, which makes it difficult to learn the boundaries of different categories. In this paper, we propose a topology-guided graph neural network, MTGNet, which treats the mesh quality evaluation task as a multi-label task. Specifically, we first decomposed the categories in traditional multi-classification problems and obtained three completely orthogonal mesh quality labels, namely orthogonality, smoothness and, distribution. Then, MTGNet introduces a topology-guided feature representation for structured mesh data, which can generate multiple blocks of element-based graphs through the mesh topology. In order to better fuse features in different blocks, MTGNet also introduces an attention-based block graph pooling (ABGPool) method. Experimental results on the NACA-Market dataset demonstrate MTGNet shows superior or at least comparable performance to the state-of-the-art (SOTA) approaches.