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.
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Linear theory predicts regimes for deformable surfaces in turbulence where the interface is enslaved by flow or shows intrinsic dynamics; simulations of air-water and rubber match predictions without wave turbulence.
citing papers explorer
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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.
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Free surfaces in turbulence -- A unified framework from water surfaces to elastic solids
Linear theory predicts regimes for deformable surfaces in turbulence where the interface is enslaved by flow or shows intrinsic dynamics; simulations of air-water and rubber match predictions without wave turbulence.