A tensorized Fourier neural operator trained with an H2 loss predicts macroscopic permeability from Stokes-Brinkman coefficient fields with about 9% relative error on log-permeability.
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Operator learning regularization for macroscopic permeability prediction in dual-scale flow problem
A tensorized Fourier neural operator trained with an H2 loss predicts macroscopic permeability from Stokes-Brinkman coefficient fields with about 9% relative error on log-permeability.