构建汽车气动性能AI模型评估框架，提升研究透明度与可比性。
A Benchmarking Framework for AI models in Automotive Aerodynamics
- 基于PhysicsNeMo-CFD搭建开放可扩展评估框架
- 在DrivAerML数据集上测试3类AI模型的流场预测精度
- 支持工业界与学术界选择、优化AI气动建模方法
本文在开源NVIDIA PhysicsNeMo-CFD框架内提出一个基准评估框架，系统评测AI模型在汽车气动预测中的准确性、性能、可扩展性及泛化能力。该开放可扩展框架支持计算机辅助工程（CAE）领域多样化的评价指标。通过提供标准化的模型比较方法，提升评估透明度与一致性，推动该领域研究与创新。为验证其有效性，框架对三种AI模型——DoMINO、X-MeshGraphNet和FIGConvNet——在DrivAerML数据集上进行了表面与体积分量流场预测评估，并提供集成新模型与数据集的指南，支持物理一致性指标扩展。本研究旨在帮助研究人员与工业界选择、优化并推进基于AI的气动建模技术，最终实现更高效、准确、可解释的汽车气动解决方案。
原文摘要 · Abstract (English)
In this paper, we introduce a benchmarking framework within the open-source NVIDIA PhysicsNeMo-CFD framework designed to systematically assess the accuracy, performance, scalability, and generalization capabilities of AI models for automotive aerodynamics predictions. The open extensible framework enables incorporation of 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 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
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