Abstract
In this paper, we develop a novel structured mesh generation method, MeshNet. The core of the proposed method is the introduction of deep neural networks to learn high-quality meshing rules and generate desired meshes. To accomplish this, MeshNet employs a well-designed physics-informed neural network to approximate the potential transformation (mapping) between computational and physical domains. The training process is governed by differential equations, boundary conditions, and a priori data derived from coarse mesh generation, which has been disregarded in previous studies. The automatic subdivision of a given domain into quadrilateral elements is achieved through efficient feed-forward neural prediction. A series of experiments are conducted to investigate the robustness of the proposed method. The results across different cases demonstrate that MeshNet is fast and robust. It outperforms state-of-the-art neural network-based generators and produces meshes of comparable or higher quality compared to expensive traditional meshing methods. Furthermore, the proposed method enables fast varisized mesh generation without re-training. The simplicity and computational efficiency of MeshNet make it a novel meshing tool in the discretization part of simulation software.
Funder
National Key Research and Development Program of China
Subject
Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering
Cited by
3 articles.
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