We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process, while emphasizing that our approach can be readily extended to other engineering and design domains. Our framework integrates AI-driven design agents into the traditional engineering workflow, demonstrating how these specialized computational agents interact seamlessly with engineers and designers to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle. By automating and streamlining tasks traditionally performed manually, such as conceptual sketching, styling enhancements, 3D shape retrieval and generative modeling, computational fluid dynamics (CFD) meshing, and aerodynamic simulations, our approach reduces certain aspects of the conventional workflow from weeks and days down to minutes. These agents leverage state-of-the-art vision-language models (VLMs), large language models (LLMs), and geometric deep learning techniques, providing rapid iteration and comprehensive design exploration capabilities. We ground our methodology in industry-standard benchmarks, encompassing a wide variety of conventional automotive designs, and utilize high-fidelity aerodynamic simulations to ensure practical and applicable outcomes. Furthermore, we present design agents that can swiftly and accurately predict simulation outcomes, empowering engineers and designers to engage in more informed design optimization and exploration. This research underscores the transformative potential of integrating advanced generative AI techniques into complex engineering tasks, paving the way for broader adoption and innovation across multiple engineering disciplines.

翻译：本文提出"设计智能体"概念，旨在解决工程应用领域的问题，特别聚焦于汽车设计流程，同时强调该方法可轻松扩展至其他工程与设计领域。该框架将AI驱动的设计智能体整合至传统工程工作流中，展示了这些专用计算智能体如何与工程师和设计师无缝交互，从而增强创造力、提升效率，并显著加速整体设计周期。通过自动化并优化传统上需手动执行的任务——包括概念草图绘制、造型优化、三维形状检索与生成建模、计算流体力学网格划分以及空气动力学仿真——本方法将传统工作流中特定环节从数周或数天缩短至分钟级别。这些智能体依托前沿的视觉语言模型、大语言模型及几何深度学习技术，提供快速迭代与全面设计探索能力。本方法基于行业标准基准测试，涵盖多种传统汽车设计类型，并采用高保真空气动力学仿真以确保成果的实用性与适用性。此外，我们提出的设计智能体能够快速精准预测仿真结果，助力工程师与设计师进行更具前瞻性的设计优化与探索。本研究揭示了将先进生成式AI技术融入复杂工程任务的变革潜力，为跨工程学科的更广泛应用与创新开辟了道路。