Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation

• URL: http://arxiv.org/abs/2606.10601v1
• Date: Tue, 09 Jun 2026 09:04:32 GMT
• Title: Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
• Abstract summary: We introduce Dmsh, a first fully automated reinforcement learning pipeline that unifies geometric decomposition and quadrilateral mesh generation.<n>Dmsh decomposes the problem through three coordinated agents handling topology simplification, geometric regularization, and mesh generation.<n>A learning strategy ensures scalability from simple domains to highly complex geometries, suppressing seed variance.
• Score: 0.30786914102688595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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