Ordered Physics Mamba: A State–space Model for Vehicle Flow Field and Aerodynamic Force Prediction

The rapid and accurate prediction of vehicle flow field distributions and aerodynamic force coefficients is of great importance in aerodynamic design scenarios. The current deep learning-based surrogate models face three major challenges when handling large-scale irregular meshes: low computational efficiency, the absence of physics priors that are aligned with the dominant flow direction, and the tendency to lose high-frequency geometric details on the surface of the target body. Considering the above issues, the ordered physics Mamba (OP-Mamba) flow field prediction model is proposed in this work based on selective state–space sequence modeling. First, OP-Mamba clusters discrete point clouds into a compact set of physics-aware slices through adaptive soft assignment, thereby reducing the imposed computational burden. A spatial sorting mechanism based on the weighted geometric centroids of these slices is then introduced to reorder them along the principal flow axis. Second, combined with this reordering scheme, bidirectional Mamba scanning not only captures the downstream convective evolution process but also provides a direct backward path for incorporating the nonlocal pressure effects that are associated with the downstream wake structures, thereby efficiently extracting long-range dependencies. Finally, OP-Mamba uses a point cloud-domain residual compensation scheme in which feature mappings and skip connections are implemented in the original discrete space, thus mitigating the high-frequency geometric feature attenuation that is caused by slice-level dimensionality reduction. Extensive experiments conducted on three-dimensional (3D) industrial vehicle datasets (including Baidu CIKM and ShapeNet-Car) and the two-dimensional (2D) airfoil dataset (AirfRANS) demonstrate that OP-Mamba achieves state-of-the-art accuracy in both flow field reconstruction and macroscopic aerodynamic force prediction tasks. Additionally, it maintains competitive performance under physical-field noise augmentation during the training procedure and demonstrates its applicability to both 3D vehicle and 2D airfoil settings.