Unsteady flow predictions around an obstacle using Geometry-Parameterized Dual-Encoder Physics-Informed Neural Network

Abstract: Machine learning-based flow field prediction is emerging as a promising alternative to traditional Computational Fluid Dynamics, offering significant computational efficiency advantage. In this work, we propose the Geometry-Parameterized Dual-Encoder Physics-Informed Neural Network (GP-DE-PINN) with a dual-encoder architecture for effective prediction of unsteady flow fields around parameterized geometries. This framework integrates a geometric parameter encoder to map low-dimensional shape parameters to high-dimensional latent features, coupled with a spatiotemporal coordinate encoder, and is trained under the Navier-Stokes equation constraints. Using 2D unsteady flow past petal-shaped cylinders as an example, we evaluate the model's reconstruction performance, generalization capability, and hyperparameter sensitivity. Results demonstrate that the GP-DE-PINN significantly outperforms the PINN with direct geometric input in flow field reconstruction, accurately capturing vortex shedding structures and pressure evolution, while exhibiting superior generalization accuracy on unseen geometric configurations. Furthermore, sensitivity analyses regarding geometric sampling and network width reveal the model's robustness to these hyperparameter variations. These findings illustrate that the proposed framework can serve as a robust and promising framework for predicting unsteady flows around complex geometric obstacles.