TripNet: Learning large-scale high-fidelity three-dimensional car aerodynamics with triplane networks

Surrogate modeling has become a powerful approach for accelerating computational fluid dynamics (CFD) simulations, yet existing three-dimensional (3D) geometric learning models–based on meshes, point clouds, or voxels–remain fundamentally limited by resolution, memory cost, and dependence on explicit geometric discretizations. We introduce TripNet, a triplane-based neural framework that encodes 3D geometry into a compact, continuous, resolution-independent representation. Unlike traditional mesh-dependent models, TripNet supports query-based predictions at arbitrary spatial locations, enabling high-fidelity aerodynamic field estimation without relying on mesh connectivity or downsampled geometry. TripNet achieves state-of-the-art performance on the DrivAerNet and DrivAerNet++ datasets across three major aerodynamic tasks: drag coefficient prediction, surface pressure and wall-shear stress regression, and full 3D volumetric flow-field prediction. TripNet attains an R2 of 0.972 for drag prediction and a relative L2 error of 10.39% for velocity magnitude. TripNet reduces inference time by several orders of magnitude–predicting drag in 0.01 s and full 3D flow fields in 2 s on a single graphics processing unit–while improving accuracy and reducing memory cost compared to strong baselines such as FigConvNet, Transolver, and MeshGraphNet. Beyond in-distribution prediction, we demonstrate that triplane features encode robust, transferable geometric–aerodynamic priors. TripNet exhibits strong cross-shape generalization, outperforming prior works when trained on two car-body classes and tested on an unseen third shape. Furthermore, TripNet achieves data-efficient cross-dataset transfer: when pretrained on DrivAerNet++ and finetuned on the high-fidelity DrivAerML dataset, TripNet surpasses models trained solely on DrivAerML across all metrics, even with as few as 10–50 gradient steps. Collectively, these results highlight triplanes as a scalable, generalizable representation for partial differential equation surrogate modeling and establish TripNet as an accurate, efficient alternative to traditional CFD solvers and existing geometric deep learning models.