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

TL;DR

The paper addresses the CAD-to-mesh bottleneck in engineering simulation by surveying AI methods across part recognition, meshing, and automation. It surveys approaches ranging from classical ML to deep learning on geometric representations, reinforcement learning for meshing decisions, and large language models for scripting. Key contributions include a taxonomy of AI methods, synthesis of representative methods and deployments, and a forward-looking set of research challenges such as data curation and standardized benchmarks. The work argues that AI will augment, not replace, traditional geometry kernels, enabling more automated, reproducible, and data-driven meshing workflows with practical impact in industrial CAE.

Abstract

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.