most citedTurbulence Closure in RANS and Flow Inference around a Cylinder using PINNs and Sparse Experimental Data

2 citations · 2 across the 4 of their papers we have counts for

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physics.flu-dyn2026

Generalizable turbulence closures across bluff-body shapes by PINN-based solver-agnostic training

Zhen Zhang, Theo Käufer, Louise Ronglan +2

Data-driven turbulence closures are commonly calibrated by embedding a CFD solver within an inverse procedure that requires a converged flow solution at every optimization iteratio…

physics.flu-dyn2026

Turbulence Physics Governs a Scaling Law for the Machine-Learning Predictability Ceiling in Chaotic Flow

Jiashun Guan, Haoyang Hu, Yunxiao Ren +2

For centuries, the intrinsic chaos of unsteady fluid motion has stood as a formidable barrier to long-term forecasting. While machine learning (ML) has recently emerged as a transf…

physics.flu-dyn2025★ 2 cited

Turbulence Closure in RANS and Flow Inference around a Cylinder using PINNs and Sparse Experimental Data

Z. Zhang, K. Shukla, Z. Wang +8

Traditional Reynolds-averaged Navier-Stokes (RANS) closures, based on the Boussinesq eddy viscosity hypothesis and calibrated on canonical flows, often yield inaccurate predictions…