Turbulence closure in Reynolds-averaged Navier–Stokes and flow inference around a cylinder using physics-informed neural networks and sparse experimental data

Traditional Reynolds-averaged Navier–Stokes (RANS) closures, based on the Boussinesq eddy-viscosity hypothesis and calibrated on canonical flows, often yield inaccurate predictions of both mean flow and turbulence statistics. Here, we consider flow past a circular cylinder over a range of Reynolds numbers ( $3900$ – $100\,000$ ) and Mach numbers ( $0$ – $0.3$ ), encompassing incompressible and weakly compressible regimes, with the goal of improving predictions of mean velocity and Reynolds force