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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.23719 |
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| _version_ | 1866912863561449472 |
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| author | Owen, Steven Brown, Nathan Chrisochoides, Nikos Garimella, Rao Gu, Xianfeng Ledoux, Franck Lei, Na Quadros, Roshan Ray, Navamita Winovich, Nicolas Zhang, Yongjie Jessica |
| author_facet | Owen, Steven Brown, Nathan Chrisochoides, Nikos Garimella, Rao Gu, Xianfeng Ledoux, Franck Lei, Na Quadros, Roshan Ray, Navamita Winovich, Nicolas Zhang, Yongjie Jessica |
| contents | Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, block-structured meshing in 2D and 3D, and volumetric parameterization, including reconstruction from implicit or sampled geometry. We also discuss parallel mesh generation and scripting automation via reinforcement learning and large language models. Across these topics, AI complements established geometry and meshing algorithms rather than replacing them. We conclude with practical lessons and open challenges in data, benchmarks, and trustworthy integration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23719 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation Owen, Steven Brown, Nathan Chrisochoides, Nikos Garimella, Rao Gu, Xianfeng Ledoux, Franck Lei, Na Quadros, Roshan Ray, Navamita Winovich, Nicolas Zhang, Yongjie Jessica Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning 51-02 Research exposition (monographs, survey articles) pertaining to geometry A.1 Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, block-structured meshing in 2D and 3D, and volumetric parameterization, including reconstruction from implicit or sampled geometry. We also discuss parallel mesh generation and scripting automation via reinforcement learning and large language models. Across these topics, AI complements established geometry and meshing algorithms rather than replacing them. We conclude with practical lessons and open challenges in data, benchmarks, and trustworthy integration. |
| title | A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning 51-02 Research exposition (monographs, survey articles) pertaining to geometry A.1 |
| url | https://arxiv.org/abs/2512.23719 |