  Vortex-induced vibration (VIV) exists widely in natural and industrial
fields. The main approaches for solving VIV problems are numerical simulations
and experimental methods. However, experiment methods are difficult to obtain
the whole flow field information and also high-cost while numerical simulation
is extraordinary time-consuming and limited in low Reynolds number and simple
geometric configuration. In addition, numerical simulations are difficult to
handle the moving mesh technique. In this paper, physics informed neural
network (PINN) is proposed to solve the VIV and wake-induced vibration (WIV) of
cylinder with high Reynolds number. Compared to tradition data-driven neural
network, the Reynolds Average Navier-Stokes (RANS) equation, by implanting an
additional turbulent eddy viscosity, coupled with structure's dynamic motion
equation are also embedded into the loss function. Training and validation data
is obtained by computational fluid dynamic (CFD) technique. Three scenarios are
proposed to validate the performance of PINN in solving VIV and WIV of
cylinders. In the first place, the stiffness parameter and damping parameter
are calculated via limited force data and displacement data; secondly, the flow
field and lifting force/drag force are inferred by scattered velocity
information; eventually, the displacement can be directly predicted only
through lifting forces and drag forces based on LSTM. Results demonstrate
that,compared with traditional neural network, PINN method is more effective in
inferring and re-constructing the unknown parameters and flow field with high
Reynolds number under VIV and WIV circumstances.
