CANTO: CAD-Native Transformer Operators for AI-Aided Engineering

Modern engineering systems, from automobiles to aircraft, are defined by precise parametric computer-aided design (CAD) models. Optimizing these designs requires iterative geometric updates followed by simulation-based validation, a process that typically begins with mesh generation. In practice, this involves discretizing the updated geometry, replacing the exact representation with a sampled approximation. This meshing step is computationally expensive, brittle under geometric variation, often dependent on manual intervention, and introduces information loss before analysis begins. Existing AI surrogates promise large computational speedups to the simulation, but inherit the same representation gap by relying on meshes, point clouds, voxels, or other sampled approximations of geometry. We introduce CANTO, a neural operator transformer that maps directly from continuous CAD geometry to physical fields, without meshing at any stage of the surrogate pipeline. We formalize CAD-to-physics surrogate modeling as operator learning between sequences of parametric NURBS patches and function spaces of physical fields. CANTO introduces a NURBS-native tokenization and predicts continuous fields and engineering quantities at arbitrary query locations. We validated the performance of CANTO on several automotive and aircraft aerodynamics industry benchmarks: the AhmedML, WindsorML, DrivaerML and HiLiftAeroML. In all cases, CANTO outperforms or is on par with the state-of-the-art for all variables for both surface and volume prediction. In particular, on HiLiftAeroML, it achieves a 19.8% relative improvement on the surface pressure prediction, without first meshing or tessellating the CAD geometry. This opens the door to AI-aided engineering systems and foundation models that operate directly on CAD, enabling real-time feedback, optimization, and inverse design.