Extending a Physics-informed Machine-learning Network for Superresolution Studies of Rayleigh–Bénard Convection
Diane M. Salim,
Blakesley Burkhart,
David Sondak
Abstract:Advancing our understanding of astrophysical turbulence is bottlenecked by the limited resolution of numerical simulations that may not fully sample scales in the inertial range. Machine-learning (ML) techniques have demonstrated promise in upscaling resolution in both image analysis and numerical simulations (i.e., superresolution). Here we employ and further develop a physics-constrained convolutional neural network ML model called “MeshFreeFlowNet” (MFFN) for superresolution studies of turbulent systems. Th… Show more
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