基于生成式AI的提示进化工程设计优化：结合视觉-语言模型
Generative AI-based Prompt Evolution Engineering Design Optimization With Vision-Language Model
AI总结：
本文提出PEDO框架，将进化策略、物理求解器、CFD仿真与视觉-语言模型结合，通过文本提示进化优化汽车三维设计，使实用设计生成概率较基线提高超20%。
摘要：
工程设计优化需要高效结合三维形状表示、优化算法和设计性能评估方法，而这通常计算成本高昂。我们提出一种以车辆设计场景为背景的提示进化设计优化（PEDO）框架，该框架利用视觉-语言模型对生成模型合成的不切实际的汽车设计施加惩罚。框架主干是一种进化策略，并与优化目标函数耦合；该目标函数包含基于物理的求解器以及视觉-语言模型，用于为生成的汽车设计提供实用性或功能性引导。在提示进化搜索中，优化器迭代生成一组文本提示，这些提示嵌入用户对三维汽车设计空气动力学性能和视觉偏好的规格要求。随后，除计算流体动力学仿真外，还使用预训练视觉-语言模型惩罚不切实际的设计，从而促使进化算法寻找更可行的设计。我们在汽车设计优化问题上的研究表明，搜索早期生成的潜在汽车设计分布广泛，说明初始种群具有良好的设计多样性；与未使用视觉-语言模型的基线框架相比，生成实用设计的概率提高超过20%。对照性能结果对设计进行视觉检查表明，提示进化是一种极具前景的范式，可发现具有良好优化性能的新颖设计，同时通过自然语言界面便捷地指定设计规格和偏好。
英文摘要：
Engineering design optimization requires an efficient combination of a 3D shape representation, an optimization algorithm, and a design performance evaluation method, which is often computationally expensive. We present a prompt evolution design optimization (PEDO) framework contextualized in a vehicle design scenario that leverages a vision-language model for penalizing impractical car designs synthesized by a generative model. The backbone of our framework is an evolutionary strategy coupled with an optimization objective function that comprises a physics-based solver and a vision-language model for practical or functional guidance in the generated car designs. In the prompt evolutionary search, the optimizer iteratively generates a population of text prompts, which embed user specifications on the aerodynamic performance and visual preferences of the 3D car designs. Then, in addition to the computational fluid dynamics simulations, the pre-trained vision-language model is used to penalize impractical designs and, thus, foster the evolutionary algorithm to seek more viable designs. Our investigations on a car design optimization problem show a wide spread of potential car designs generated at the early phase of the search, which indicates a good diversity of designs in the initial populations, and an increase of over 20\% in the probability of generating practical designs compared to a baseline framework without using a vision-language model. Visual inspection of the designs against the performance results demonstrates prompt evolution as a very promising paradigm for finding novel designs with good optimization performance while providing ease of use in specifying design specifications and preferences via a natural language interface.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
