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

Abstract This paper discusses a physics-informed surrogate model aimed at reconstructing the flow field from sparse datasets under a limited computational budget. A benchmark problem of 2D unsteady laminar flow past a cylinder is chosen to evaluate the performance of the surrogate model. Earlier studies were focused on forward problems with well-defined data. The present study attempts to develop models capable of reconstructing the flow-field data from sparse datasets mirroring real-world scenarios. We demonstrated the performance of data-driven models in reconstructing the flow field and compared the effectiveness of various training methodologies. The proposed surrogate model successfully reconstructed the flow field while also extracting pressure as a latent variable. The proposed surrogate model significantly outperformed data-driven models in accuracy, even under a limited computational budget. Furthermore, transfer learning of parameters of a pretrained model for different Reynolds numbers has reduced training time.