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

Abstract

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.