  Vortex-induced vibration (VIV) is a typical nonlinear fluid-structure
interaction phenomenon, which widely exists in practical engineering (the
flexible riser, the bridge and the aircraft wing, etc). The conventional finite
element model (FEM)-based and data-driven approaches for VIV analysis often
suffer from the challenges of the computational cost and acquisition of
datasets. This paper proposed a transfer learning enhanced the physics-informed
neural network (PINN) model to study the VIV (2D). The physics-informed neural
network, when used in conjunction with the transfer learning method, enhances
learning efficiency and keeps predictability in the target task by common
characteristics knowledge from the source model without requiring a huge
quantity of datasets. The datasets obtained from VIV experiment are divided
evenly two parts (source domain and target domain), to evaluate the performance
of the model. The results show that the proposed method match closely with the
results available in the literature using conventional PINN algorithms even
though the quantity of datasets acquired in training model gradually becomes
smaller. The application of the model can break the limitation of monitoring
equipment and methods in the practical projects, and promote the in-depth study
of VIV.
