This paper proposes an unsupervised mesh movement network with a novel M-Uniform loss, achieving generalization across diverse PDEs and mesh geometries without labeled data.

We propose a data augmentation method for zero-shot generalization in reinforcement learning and provide a theoretical and empirical analysis of the method.