A CTA-Swin-UNet with MTFC correction and resolvent-based SLSE reconstruction achieves stable autoregressive prediction of 3D wall-bounded turbulence up to 300 time steps.
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physics.flu-dyn 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Symbolic regression with built-in physical constraints produces a non-linear turbulence closure for LBM that outperforms Smagorinsky and generalizes zero-shot to channel flow.
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Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis
A CTA-Swin-UNet with MTFC correction and resolvent-based SLSE reconstruction achieves stable autoregressive prediction of 3D wall-bounded turbulence up to 300 time steps.
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Data-driven Symbolic Closure for Turbulence Modeling in the Lattice Boltzmann Framework
Symbolic regression with built-in physical constraints produces a non-linear turbulence closure for LBM that outperforms Smagorinsky and generalizes zero-shot to channel flow.