Robust reduced-order modeling for shock-dominated compressible flows over a range of parameters

This paper focuses on the reduced-order modeling of strongly compressible flows over a range of parameters based on the combination of proper orthogonal decomposition (POD) and Kolmogorov–Arnold networks (KAN). Precisely, the present method first utilizes POD to extract the dominant modal features of the flow field. Then, the powerful high-dimensional nonlinear mapping capability of KAN is leveraged to reconstruct the complex dynamic behavior of shock waves. The results of several typical hypersonic flows imply that the proposed reduced-order model (ROM) significantly improves computational efficiency while demonstrating higher prediction accuracy than most existing intelligent ROM methods. More importantly, the present ROM can also effectively predict the flow fields for many unseen flow parameters. These results highlights the generalization performance of the proposed method and its remarkable advantages in predicting complex compressible flows.