Rotation-and-reflection-invariant machine learning inputs improve a priori prediction of the signed Smagorinsky coefficient, including backscatter regions, for mesoscale hurricane boundary layer flows.
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Invariance-embedded Machine Learning Sub-grid-scale Stress Models for Meso-scale Hurricane Boundary Layer Flow Simulation I: Model Development and $\textit{a priori}$ Studies
Rotation-and-reflection-invariant machine learning inputs improve a priori prediction of the signed Smagorinsky coefficient, including backscatter regions, for mesoscale hurricane boundary layer flows.