Work

Optimization of a Triangular Delaunay Mesh Generator using Reinforcement Learning

Year: 2025

Type: article

Abstract: In this work we introduce a triangular Delaunay mesh generator that can be trained using reinforcement learning to maximize a given mesh quality metric. Our mesh generator consists of a graph neural network that distributes and modifies vertices, and a standard Delaunay algorithm to triangulate the vertices. We explore various design choices and evaluate our mesh generator on various tasks including mesh generation, mesh improvement, and producing variable resolution meshes. The learned mesh generator outputs meshes that are comparable to those produced by Triangle and DistMesh, two popular Delaunay-based mesh generators.

Source: Computer-Aided Design

DOI: 10.1016/j.cad.2025.103964

Authors: Will Thacher, Yulong Pan, Per-Olof Persson

Institutions: Lawrence Berkeley National Laboratory, University of California, Berkeley

Language: English

Topic: Computational Geometry and Mesh Generation

Subfield: Computer Graphics and Computer-Aided Design

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,140

OpenAlex ID: W4413915946

FWCI: 6.698

Cites: 26

Cited by: 3

Related to: 10