Sparse regression yields explicit invariant polynomial SGS closures for LES on anisotropic grids that achieve neural-network accuracy with simpler forms and lower computational cost.
Sagaut, Large Eddy Simulation for Incompressible Flows, no
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An a posteriori framework implemented in PyMHD estimates numerical dissipation in Alfvénic, dynamo, and MRI-driven MHD turbulence, showing it has distinct spectral and anisotropic properties from physical dissipation.
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Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids
Sparse regression yields explicit invariant polynomial SGS closures for LES on anisotropic grids that achieve neural-network accuracy with simpler forms and lower computational cost.