A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation

TL;DR

This survey reviews recent AI techniques applied to various stages of geometry preparation and mesh generation in engineering simulation, highlighting AI's role in enhancing traditional methods.

Contribution

It systematically organizes recent AI methods across the entire workflow of geometry and mesh generation, emphasizing AI's complementary role and discussing practical challenges.

Findings

AI aids in part classification and segmentation

AI improves mesh quality prediction and defeaturing

AI-guided meshing enhances automation and efficiency

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.