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

Dmsh is a newly introduced multi-agent reinforcement learning framework designed for fully automated all-quadrilateral mesh generation. It utilizes a Soft Actor-Critic architecture to address the meshing process as a Markov Decision Process, employing three coordinated agents for topology simplification, geometric regularization, and mesh generation, while implementing a curriculum learning strategy to enhance scalability. This framework significantly surpasses traditional methods in automation, robustness, and mesh quality, providing a novel approach for practitioners dealing with complex geometries in computational engineering.