A physics-informed graph neural network with two-phase training and physics-preserving normalization estimates hydraulic states in water networks, improving scalability and out-of-distribution robustness over the prior PI-GNN.
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Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems
A physics-informed graph neural network with two-phase training and physics-preserving normalization estimates hydraulic states in water networks, improving scalability and out-of-distribution robustness over the prior PI-GNN.