  Physics-informed neural networks (PINNs) provide a framework to build
surrogate models for dynamical systems governed by differential equations.
During the learning process, PINNs incorporate a physics-based regularization
term within the loss function to enhance generalization performance. Since
simulating dynamics controlled by partial differential equations (PDEs) can be
computationally expensive, PINNs have gained popularity in learning parametric
surrogates for fluid flow problems governed by Navier-Stokes equations. In this
work, we introduce RANS-PINN, a modified PINN framework, to predict flow fields
(i.e., velocity and pressure) in high Reynolds number turbulent flow regimes.
To account for the additional complexity introduced by turbulence, RANS-PINN
employs a 2-equation eddy viscosity model based on a Reynolds-averaged
Navier-Stokes (RANS) formulation. Furthermore, we adopt a novel training
approach that ensures effective initialization and balance among the various
components of the loss function. The effectiveness of the RANS-PINN framework
is then demonstrated using a parametric PINN.
