{"ID":2828692,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23719","arxiv_id":"2512.23719","title":"A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2512.23719","url_pdf":"https://arxiv.org/pdf/2512.23719v2","authors":"[\"Steven Owen\",\"Nathan Brown\",\"Nikos Chrisochoides\",\"Rao Garimella\",\"Xianfeng Gu\",\"Franck Ledoux\",\"Na Lei\",\"Roshan Quadros\",\"Navamita Ray\",\"Nicolas Winovich\",\"Yongjie Jessica Zhang\"]","published":"2025-12-16T20:51:54Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}