  We propose and validate a data-driven approach for modeling large-amplitude
flow-induced oscillations of elastically mounted pitching wings. We first train
a neural networks regression model for the nonlinear aerodynamic moment using
data obtained from purely prescribed oscillations and static measurements. We
then embed this model into an ordinary differential equation solver to solve
for the governing equation of the passive aeroelastic system with desired
structural parameters. The system dynamics predicted by the proposed
data-driven method are characterized and compared with those obtained from
physical experiments. The predicted and experimental pitching amplitude,
frequency and aerodynamic moment responses are found to be in excellent
agreement. Both the structural mode of high-inertia wings and the hydrodynamic
mode of low-inertia wings are successfully predicted. The transient growth and
saturation of the oscillation amplitude and the aerodynamic moment are also
faithfully captured by the proposed method. Additional test cases demonstrate
the broad applicability of this approach.
