Trailblazing specific generative models (SGMs) for early-stage engineering design concepts - Obieke, Chijioke C.; Milisavljevic-Syed, Jelena; Jiang, Pingfei; Bridgeman, John; Han, Ji (Elsevier, 2025)
Recent advancements in large language models (LLMs) have the potential to revolutionise engineering design practices. LLM-powered tools such as ChatGPT and DeepSeek have shown significant promise in enhancing various aspects of engineering design. They leverage emergent data-driven technologies to deliver powerful capabilities that challenge the long-held belief that generating new concepts and automating engineering design activities are beyond the reach of artificial intelligence (AI). The capabilities demonstrated by these technologies are gradually overcoming initial resistance and fostering a more receptive attitude towards their adoption. However, a substantial gap remains between the generic capabilities of AI technologies and their specific application in engineering design. This study aims to leverage a combination of AI technologies to support early-stage engineering design activities. Specifically, we created a generative Markov Chain model adjusted as a specific generative model (SGM) to support the complex and time-consuming conceptual early-stage design activities. The SGM does not require extensive and expensive data training like the generic LLM, but produces comparably more natural concepts. The SGM used in this study effectively addresses the privacy concerns associated with generic LLMs. This pioneering research on SGM is cost-effective and holds promise for applications beyond engineering design, including fields such as medicine.
Keywords: 40 Engineering; 33 Built Environment and Design; 4010 Engineering Practice and Education; 3303 Design; Bioengineering; Machine Learning and Artificial Intelligence; 4 Quality Education; AI collaboration; Specific generative models; Concept generation; Early-stage design