G-PARC embeds analytically computed differential operators via moving least squares on graphs into recurrent networks, achieving higher accuracy with 2-3x fewer parameters than prior graph PADL methods on nonlinear benchmarks.
Parc: Physics-aware recurrent convolutional neural networks to assimilate meso scale reactive mechanics of energetic materials.Science advances, 9(17):eadd6868
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G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
G-PARC embeds analytically computed differential operators via moving least squares on graphs into recurrent networks, achieving higher accuracy with 2-3x fewer parameters than prior graph PADL methods on nonlinear benchmarks.