Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations

Eivazi H, Tahani M, Schlatter P, Vinuesa R (2022)

Publication Type: Journal article

Publication year: 2022


Book Volume: 34

Article Number: 075117

Journal Issue: 7

DOI: 10.1063/5.0095270


Physics-informed neural networks (PINNs) are successful machine-learning methods for the solution and identification of partial differential equations. We employ PINNs for solving the Reynolds-averaged Navier-Stokes equations for incompressible turbulent flows without any specific model or assumption for turbulence and by taking only the data on the domain boundaries. We first show the applicability of PINNs for solving the Navier-Stokes equations for laminar flows by solving the Falkner-Skan boundary layer. We then apply PINNs for the simulation of four turbulent-flow cases, i.e., zero-pressure-gradient boundary layer, adverse-pressure-gradient boundary layer, and turbulent flows over a NACA4412 airfoil and the periodic hill. Our results show the excellent applicability of PINNs for laminar flows with strong pressure gradients, where predictions with less than 1% error can be obtained. For turbulent flows, we also obtain very good accuracy on simulation results even for the Reynolds-stress components.

Authors with CRIS profile

Involved external institutions

How to cite


Eivazi, H., Tahani, M., Schlatter, P., & Vinuesa, R. (2022). Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations. Physics of Fluids, 34(7).


Eivazi, Hamidreza, et al. "Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations." Physics of Fluids 34.7 (2022).

BibTeX: Download