| DOI | Trouver le DOI : https://doi.org/10.1017/S089006042100007X |
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| Auteur | Rechercher : Pan, Jie; Rechercher : Huang, JingweiIdentifiant ORCID : https://orcid.org/0000-0003-2155-6107; Rechercher : Wang, Yunli1; Rechercher : Cheng, Gengdong; Rechercher : Zeng, YongIdentifiant ORCID : https://orcid.org/0000-0001-6678-271X |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Sujet | element extraction; reinforcement learning; self-learning system; smart design; smart mesh generation |
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| Résumé | 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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| Date de publication | 2021-04-21 |
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| Maison d’édition | Cambridge University Press |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Identificateur | S089006042100007X |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 4b9b710b-dda1-46cb-83b4-bc6c9483e970 |
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| Enregistrement créé | 2021-08-16 |
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| Enregistrement modifié | 2021-08-16 |
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