LSE-MGN, a graph neural network with long- and short-edge message passing, predicts pore-scale gas/liquid evolution from experimental micro-CT data with roughly 9 to 10 percent surface-area error over short autoregressive rollouts.
A review of cell-scale multiphase flow modeling, including water management, in polymer electrolyte fuel cells
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Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network
LSE-MGN, a graph neural network with long- and short-edge message passing, predicts pore-scale gas/liquid evolution from experimental micro-CT data with roughly 9 to 10 percent surface-area error over short autoregressive rollouts.