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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 usually calibrated by inverse methods that embed a CFD solver in the loop, tying the model to a particular discretization and requiring every it…
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
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…