M. Tahani

Turbulence modeling using physics-informed neural networks: The importance of training points distribution and hyper parameters

Physics-informed neural networks (PINNs) are employed to solve the Navier–Stokes equations with a standard k–ω turbulence model for flow over a period, focusing on how the spatial placement of training points and hyperparameters affect accuracy. Wall-bounded turbulence modeling includes challenges because variables lik...