The rapid research and development of generative artificial intelligence has enabled the generation of high-quality images, text, and 3D models from text prompts. This advancement impels an inquiry into whether these models can be leveraged to create digital artifacts for both creative and engineering applications. Drawing on innovative designs from other domains may be one answer to this question, much like the historical practice of ``bionics", where humans have sought inspiration from nature's exemplary designs. This raises the intriguing possibility of using generative models to simultaneously tackle design tasks across multiple domains, facilitating cross-domain learning and resulting in a series of innovative design solutions. In this paper, we propose LLM2FEA as the first attempt to discover novel designs in generative models by transferring knowledge across multiple domains. By utilizing a multi-factorial evolutionary algorithm (MFEA) to drive a large language model, LLM2FEA integrates knowledge from various fields to generate prompts that guide the generative model in discovering novel and practical objects. Experimental results in the context of 3D aerodynamic design verify the discovery capabilities of the proposed LLM2FEA. The designs generated by LLM2FEA not only satisfy practicality requirements to a certain degree but also feature novel and aesthetically pleasing shapes, demonstrating the potential applications of LLM2FEA in discovery tasks.

翻译：生成式人工智能的快速发展使得从文本提示生成高质量图像、文本和3D模型成为可能。这一进步促使我们探究是否可以利用这些模型为创意和工程应用创建数字产物。借鉴其他领域的创新设计可能是该问题的一种解答，正如历史上“仿生学”的实践——人类从自然界的卓越设计中寻求灵感。这引发了一个引人入胜的可能性：利用生成模型同时处理多个领域的设计任务，促进跨领域学习，并产生一系列创新设计解决方案。本文提出LLM2FEA，作为首个尝试通过跨领域知识迁移在生成模型中探索新颖设计的方法。通过利用多因子进化算法驱动大型语言模型，LLM2FEA整合多领域知识以生成提示词，引导生成模型发现新颖且实用的物体。在3D空气动力学设计背景下的实验结果验证了所提LLM2FEA的发现能力。LLM2FEA生成的设计不仅在一定程度上满足实用性要求，还具有新颖美观的形态，展现了LLM2FEA在发现任务中的潜在应用价值。