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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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.