We present a learning based framework for mesh quality improvement on unstructured triangular and quadrilateral meshes. Our model learns to improve mesh quality according to a prescribed objective function purely via self-play reinforcement learning with no prior heuristics. The actions performed on the mesh are standard local and global element operations. The goal is to minimize the deviation of the node degrees from their ideal values, which in the case of interior vertices leads to a minimization of irregular nodes.

翻译：我们提出了一种基于学习的框架，用于非结构化三角形和四边形网格的质量改善。该模型通过无先验启发式的纯自博弈强化学习，学习按照预设目标函数改善网格质量。对网格执行的操作是标准的局部和全局单元操作。其目标是最小化节点度数与其理想值之间的偏差，对于内部顶点而言，这能够有效减少不规则节点的数量。