A local entropic lattice Boltzmann scheme recovers the full-tensor anisotropic advection-diffusion equation via flux-ghost population splitting, tensorial relaxation, and an ADE-corrected entropic stabilizer, with validations on 3D benchmarks up to 10^4 anisotropy ratios.
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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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Entropic lattice Boltzmann method for general anisotropic advection--diffusion
A local entropic lattice Boltzmann scheme recovers the full-tensor anisotropic advection-diffusion equation via flux-ghost population splitting, tensorial relaxation, and an ADE-corrected entropic stabilizer, with validations on 3D benchmarks up to 10^4 anisotropy ratios.
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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.