  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.
