A new composite neural network with spatiotemporal features extraction capability for unsteady flow fields predictions

Artificial intelligence based on neural network technology has provided innovative methods for predicting unsteady flow fields. However, both purely data-driven and single physics-driven methods can only perform short-term predictions for unsteady flow fields and are unable to achieve medium- to long-term predictions. A composite neural network CNN-GRU-PINN (CGPINN) is proposed by combining convolutional neural network (CNN), gated recurrent unit (GRU), and physics-informed neural network (PINN). CNN and GRU are used to learn the spatial and temporal characteristics of unsteady flows, respectively. PINN is adopted to constrain flow field prediction data according to physical laws. The flow around a circular cylinder is employed to verify the medium- and long-term prediction performances of the CGPINN. The test results show that compared to PINN, the reconstruction accuracy of the CGPINN is improved by about 86.10% on average, and the prediction accuracy is improved by about 96.18%. Compared to pure data-driven approaches, the prediction accuracy of the CGPINN is improved by an average of 65.71%. Additionally, CGPINN exhibits better robustness, demonstrating insensitivity to variations in sample size and noise levels, thereby ensuring stable and reliable performances across diverse data conditions. This study has provided a more accurate and robust method for the reconstruction and prediction of unsteady flow fields.