On the Performance of a Data-Driven Backward Compatible Physics Informed Neural Network (BC-PINN) for Prediction of Flow Past a Cylinder

Abstract

Abstract This paper discusses a sparse data driven approach for training Physics Informed Neural Networks (PINNs) to solve the Navier-Stokes equation. A benchmark problem of 2D unsteady laminar flow past a cylinder is chosen for comparing the accuracy of existing PINN training approaches to the proposed methodology. The proposed training scheme is an improvement on the existing Backward Compatible PINN (BC-PINN) methodology. We have demonstrated that the performance of BC-PINN methodology in solving Navier Stokes equation is at a similar level to that of Standard PINN methodology using mini-batches. The proposed methodology resulted in a significant increase in accuracy in comparison with vanilla BC-PINN. Furthermore, transfer learning of parameters of a pre-trained model for different Reynolds numbers has resulted in a reduction of training time.