  We propose a novel multi-scale message passing neural network algorithm for
learning the solutions of time-dependent PDEs. Our algorithm possesses both
temporal and spatial multi-scale resolution features by incorporating
multi-scale sequence models and graph gating modules in the encoder and
processor, respectively. Benchmark numerical experiments are presented to
demonstrate that the proposed algorithm outperforms baselines, particularly on
a PDE with a range of spatial and temporal scales.
