Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial. To make generative models performance-aware and enable them to create high-performing designs, surrogate modeling of these metrics is necessary. However, the currently used representations of three-dimensional (3D) shapes either require extensive computational resources to learn or suffer from significant information loss, which impairs their effectiveness in surrogate modeling. To address this issue, we propose a new two-dimensional (2D) representation of 3D shapes. We develop a surrogate drag model based on this representation to verify its effectiveness in predicting 3D car drag. We construct a diverse dataset of 9,070 high-quality 3D car meshes labeled by drag coefficients computed from computational fluid dynamics (CFD) simulations to train our model. Our experiments demonstrate that our model can accurately and efficiently evaluate drag coefficients with an $R^2$ value above 0.84 for various car categories. Moreover, the proposed representation method can be generalized to many other product categories beyond cars. Our model is implemented using deep neural networks, making it compatible with recent AI image generation tools (such as Stable Diffusion) and a significant step towards the automatic generation of drag-optimized car designs. We have made the dataset and code publicly available at https://decode.mit.edu/projects/dragprediction/.

翻译：生成式人工智能模型在自动化创建三维形状方面取得了显著进展，这有望彻底改变汽车设计。在工程设计与优化中，评估工程指标至关重要。为了使生成模型具备性能感知能力，并能生成高性能设计，必须对这些指标进行替代建模。然而，当前使用的三维形状表示方法要么需要大量计算资源来学习，要么遭受严重的信息损失，从而削弱了它们在替代建模中的有效性。为解决这一问题，我们提出了一种新的三维形状二维表示方法。我们基于这种表示开发了一个替代阻力模型，以验证其在预测三维汽车阻力方面的有效性。我们构建了一个包含9,070个高质量三维汽车网格的多样化数据集，这些网格标有通过计算流体动力学(CFD)仿真计算出的阻力系数，用于训练我们的模型。实验表明，我们的模型能够准确高效地评估阻力系数，对于各种汽车类别，其$R^2$值超过0.84。此外，所提出的表示方法可以推广到汽车之外的许多其他产品类别。我们的模型采用深度神经网络实现，使其与近期的人工智能图像生成工具（如Stable Diffusion）兼容，并朝着自动生成阻力优化汽车设计迈出了重要一步。我们已在https://decode.mit.edu/projects/dragprediction/公开了数据集和代码。