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physics.flu-dyn2026
Data-driven Symbolic Closure for Turbulence Modeling in the Lattice Boltzmann Framework
Yujie Fu, Yihan Zhang, Wanru Deng +1
Turbulence modeling within the Lattice Boltzmann Method (LBM) framework has long relied on traditional algebraic sub-grid scale (SGS) models, which often suffer from over-dissipati…
physics.flu-dyn2024
On the Preprocessing of Physics-informed Neural Networks: How to Better Utilize Data in Fluid Mechanics
Shengfeng Xu, Chang Yan, Zhenxu Sun +3
Physics-Informed Neural Networks (PINNs) serve as a flexible alternative for tackling forward and inverse problems in differential equations, displaying impressive advancements in…