Scale-consistency training, which enforces agreement between global and rescaled sub-domain predictions, enables neural PDE solvers to extrapolate to unseen scale parameters such as Reynolds number or wavenumber.
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators.Nature Machine Intelligence, 3(3):218–229, mar 2021
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Scale-Consistent Learning for Partial Differential Equations
Scale-consistency training, which enforces agreement between global and rescaled sub-domain predictions, enables neural PDE solvers to extrapolate to unseen scale parameters such as Reynolds number or wavenumber.