A transfer learning-physics informed neural network (TL-PINN) for vortex-induced vibration

Abstract

Vortex-induced vibration (VIV) is a typical nonlinear fluid-structure interaction (FSI) phenomenon, which widely exists in practical engineering (such as flexible risers, bridges and aircraft wings). Conventional numerical simulation and data-driven approaches for VIV analysis often suffer from the challenges of computational cost and dataset acquisition. This paper proposed a physics-informed neural network (PINN) enhanced by transfer learning (TL) to study a VIV system (2D). The TL-PINN only used 1/2, 1/4 and 1/8 of the training set (for PINN model) to reconstruct the information of flow field and structure, but with the same prediction accuracy as PINN model. In addition, a stepwise iterative training strategy was proposed to train PINN model. The strategy can effectively reduce the dependence of neural networks on data sets, so as to reduce the training cost of PINN model. The results show that PINN with the stepwise iterative training strategy and TL-PINN can enhance learning efficiency and keep predictability without requiring a huge quantity of datasets. Based on the proposed method, limited and scattered label data from monitoring, numerical and experimental can be fused to realize the reconstruction and prediction of flow field and structure information. It can break the limitation of monitoring equipment and methods in practical projects, and promote the in-depth study of VIV.