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This research paper presents a learning-based framework for improving mesh quality in unstructured triangular and quadrilateral meshes using self-play reinforcement learning. The model aims to minimize irregular nodes by optimizing mesh connectivity through local and global topological operations without relying on prior heuristics. The approach is validated on various polygonal shapes, demonstrating its effectiveness in achieving optimal mesh regularity.
- Author
- weiyujiewudishuai
- Language
- EN