用强化学习自动生成高质量四边形网格，无需人工干预。
Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
- 三智能体协同分解几何并生成网格，端到端自动化
- 在复杂几何上生成全局一致的四边形网格，质量优于现有方法
- 适合需要高效高精度网格生成的工程仿真场景
为任意几何生成高质量网格仍是计算工程中的关键瓶颈，常需启发式调参和半人工流程。本文提出 Dmsh，首个完全自动化的强化学习框架，将几何分解与四边形网格生成统一于单一学习范式中。Dmsh 通过三个协同智能体分别处理拓扑简化、几何正则化和网格生成。网格生成过程建模为马尔可夫决策过程，采用参数化 Soft Actor-Critic 架构与解耦评论家，有效探索混合离散-连续动作空间。课程学习策略确保从简单到复杂几何的可扩展性，降低初始随机性影响。递归分解设计支持子区域并行网格化，生成全局共形的全四边形网格，无需后期修正。在广泛基准测试中，Dmsh 在自动化程度、鲁棒性和网格质量上持续优于现有方法，确立了学习驱动网格生成的新范式。
原文摘要 · Abstract (English)
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 exploration of a hybrid discrete-continuous action space. A curriculum learning strategy ensures scalability from simple domains to highly complex geometries, suppressing seed variance. By design, the recursive decomposition enables parallel meshing of subregions, yielding globally conforming all-quadrilateral meshes without post hoc correction. Across a wide range of benchmarks, Dmsh consistently outperforms existing methods in automation, robustness, and mesh quality, establishing a new paradigm for learning-based mesh generation.
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