| DOI | Resolve DOI: https://doi.org/10.1017/S089006042100007X |
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| Author | Search for: Pan, Jie; Search for: Huang, JingweiORCID identifier: https://orcid.org/0000-0003-2155-6107; Search for: Wang, Yunli1; Search for: Cheng, Gengdong; Search for: Zeng, YongORCID identifier: https://orcid.org/0000-0001-6678-271X |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | element extraction; reinforcement learning; self-learning system; smart design; smart mesh generation |
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| Abstract | Automatic generation of high-quality meshes is a base of CAD/CAE systems. The element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and to control local mesh densities. However, its widespread applications have been inhibited by the difficulties in generating satisfactory meshes in the interior of a domain or even in generating a complete mesh. The element extraction method's primary challenge is to define element extraction rules for achieving high-quality meshes in both the boundary and the interior of a geometric domain with complex shapes. This paper presents a self-learning element extraction system, FreeMesh-S, that can automatically acquire robust and high-quality element extraction rules. Two central components enable the FreeMesh-S: (1) three primitive structures of element extraction rules, which are constructed according to boundary patterns of any geometric boundary shapes; (2) a novel self-learning schema, which is used to automatically define and refine the relationships between the parameters included in the element extraction rules, by combining an Advantage Actor-Critic (A2C) reinforcement learning network and a Feedforward Neural Network (FNN). The A2C network learns the mesh generation process through random mesh element extraction actions using element quality as a reward signal and produces high-quality elements over time. The FNN takes the mesh generated from the A2C as samples to train itself for the fast generation of high-quality elements. FreeMesh-S is demonstrated by its application to two-dimensional quad mesh generation. The meshing performance of FreeMesh-S is compared with three existing popular approaches on ten pre-defined domain boundaries. The experimental results show that even with much less domain knowledge required to develop the algorithm, FreeMesh-S outperforms those three approaches in essential indices. FreeMesh-S significantly reduces the time and expertise needed to create high-quality mesh generation algorithms. |
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| Publication date | 2021-04-21 |
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| Publisher | Cambridge University Press |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Identifier | S089006042100007X |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 4b9b710b-dda1-46cb-83b4-bc6c9483e970 |
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| Record created | 2021-08-16 |
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| Record modified | 2021-08-16 |
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