In recent years, graph neural network (GNN) based models showed promising results in simulating complex physical systems. However, training dedicated graph network simulator can be costly, as most models are confined to fully supervised training. Extensive data generated from traditional simulators is required to train the model. It remained unexplored how transfer learning could be applied to improve the model performance and training efficiency. In this work, we introduce a pretraining and transfer learning paradigm for graph network simulator.
First, We proposed the scalable graph U-net…
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