A Benchmarking Framework for AI models in Automotive Aerodynamics

In this paper, we introduce a benchmarking framework within the open-source PhysicsNeMo-CFD framework designed to systematically and consistently assess the accuracy, performance, scalability, and generalization capabilities of AI models for automotive aerodynamics predictions. The open extensible framework enables a diverse set of metrics relevant to the Computer-Aided Engineering (CAE) community. By providing a standardized methodology for comparing AI models, the framework enhances transparency and consistency in performance assessment, with the overarching goal of improving the understanding and development of these models to accelerate research and innovation in the field.To demonstrate its utility, the framework includes evaluation of both surface and volumetric flow field predictions on three AI surrogate models — DoMINO, X-MeshGraphNet, and FIGConvNet — using the DrivAerML dataset. It also includes guidelines for integrating additional models and datasets, making it extensible for physically consistent metrics. This benchmarking study aims to enable researchers and industry professionals in selecting, refining, and advancing AI-driven aerodynamic modeling approaches, ultimately fostering the development of more efficient, accurate, and interpretable solutions in automotive aerodynamics.