计算机科学
脚本语言
钥匙(锁)
人工智能
强化学习
可用性
质量(理念)
人机交互
软件工程
工程制图
学习曲线
软件
面子(社会学概念)
作者
Steven Owen,Nathan Brown,Nikos P. Chrisochoides,Rao Garimella,Xianfeng Gu,Franck Ledoux,Na Lei,William Roshan Quadros,Navamita Ray,Nicolas Winovich,Yugang Zhang
出处
期刊:Society for Industrial and Applied Mathematics eBooks
[Society for Industrial and Applied Mathematics]
日期:2026-05-31
卷期号:: 1-21
标识
DOI:10.1137/1.9781611979138.1
摘要
Artificial intelligence is beginning to ease long-standing bottlenecks in the CAD-to-mesh pipeline. This survey reviews recent advances where machine learning aids part classification, mesh quality prediction, and defeaturing. We explore methods that improve unstructured and block-structured meshing, support volumetric parameterizations, and accelerate parallel mesh generation. We also examine emerging tools for scripting automation, including reinforcement learning and large language models. Across these efforts, AI acts as an assistive technology, extending the capabilities of traditional geometry and meshing tools. The survey highlights representative methods, practical deployments, and key research challenges that will shape the next generation of data-driven meshing workflows.
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