Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows. In this paper, we introduce Dmsh, a first fully automated reinforcement learning pipeline that unifies geometric decomposition and quadrilateral mesh generation within a single learning-based framework. Dmsh decomposes the problem through three coordinated agents handling topology simplification, geometric regularization, and mesh generation. The meshing process is formulated as a Markov Decision Process and solved using a parametric Soft Actor-Critic architecture with decoupled critics, enabling efficient explor...
| Authors | Anirudh Kalyan, Cosmin Anitescu, Xiaoying Zhuang et al. |
|---|---|
| Primary category | math.NA |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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