Applying genetic programming to outputs of a graph neural network yields compact symbolic drag-variation formulas at Reynolds numbers up to 280, though with lower accuracy than the network.
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Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches
Applying genetic programming to outputs of a graph neural network yields compact symbolic drag-variation formulas at Reynolds numbers up to 280, though with lower accuracy than the network.