  Vortex induced vibration (VIV) occurs when vortex shedding frequency falls
close to the natural frequency of a structure. Investigation on VIV is of great
value in disaster mitigation, energy extraction and other applications.
Following recent development in machine learning on VIV in laminar flow, this
study extends it to the turbulent region by employing the state-of-the-art
parameterised Navier-Stokes equations based physics informed neural network
(PNS-PINN). Turbulent flow with Reynolds number $Re$ = $10^4$, passing through
a cylinder undergoing VIV motion, was considered as an example. Within the
PNS-PINN, an artificial viscosity $\nu_t$ is introduced in the Navier-Stokes
equations and treated as a hidden output variable. A recently developed
Navier-Stokes equations based PINN, termed NSFnets (Jin \textit{et al.}, J
COMPUT PHYS 426: 109951, 2021), was also considered for comparison. Training
datasets of scattered velocity and dye trace concentration snapshots, from
computational fluid dynamics (CFD) simulations, were prepared for both the
PNS-PINN and NSFnets. Results show that, compared with the NSFnets, the
PNS-PINN is more effective in inferring and reconstructing turbulent flow under
VIV circumstances. The PNS-PINN also shows the capability to deal with unsteady
and multi-scale flows in VIV. Inspired by the Reynolds-Averaged Navier-Stokes
(RANS) formulation, by implanting an additional artificial viscosity, the
PNS-PINN is free of complex turbulence model closure, thus may be applied for
more general and complex turbulent flows.
