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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…
physics.flu-dyn2025
Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach
Qian Zhang, Dmitry Krotov, George Em Karniadakis
Machine learning methods have shown great success in various scientific areas, including fluid mechanics. However, reconstruction problems, where full velocity fields must be recov…
physics.flu-dyn2024
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks
Juan Diego Toscano, Theo Käufer, Zhibo Wang +3
We propose the Artificial Intelligence Velocimetry-Thermometry (AIVT) method to infer hidden temperature fields from experimental turbulent velocity data. This physics-informed mac…