Denoising diffusion models trained at web-scale have revolutionized image generation. The application of these tools to engineering design is an intriguing possibility, but is currently limited by their inability to parse and enforce concrete engineering constraints. In this paper, we take a step towards this goal by proposing physics-based guidance, which enables optimization of a performance metric (as predicted by a surrogate model) during the generation process. As a proof-of-concept, we add drag guidance to Stable Diffusion, which allows this tool to generate images of novel vehicles while simultaneously minimizing their predicted drag coefficients.

翻译：在大规模网络数据上训练的去噪扩散模型已经彻底改变了图像生成领域。将这些工具应用于工程设计是一个颇具前景的可能性，但目前受到其无法解析并强制执行具体工程约束的限制。在本文中，我们通过提出基于物理的引导机制，朝着这一目标迈出了一步，该机制能够在生成过程中优化性能指标（由代理模型预测）。作为概念验证，我们将拖拽引导机制添加到稳定扩散模型中，使得该工具能够生成新型车辆图像，同时最小化其预测的阻力系数。