Facilitating User-Centric Model-Based Systems Engineering Using Generative AI

The increasing complexity of cyber-physical systems requires model-based systems engineering (MBSE) in an effort to sustain a comprehensive oversight. However, broader adaptation of these models requires specialized knowledge and training. In order to make this process more user-friendly, the concept of user-centric systems engineering emerged. Artificial intelligence (AI) could help users overcome beginner hurdles and leverage their contribution quality. This research investigates the feasibility of a large language model in the systems engineering context, with a particular emphasis on the identification of potential obstacles for similar tasks. Therefore, a GPT model is trained on a dataset consisting of UML component diagram elements. In conclusion, the promising results of this research justify utilizing AI in MBSE. Complex relationships between the UML elements were not only understood, they were also generated using natural-language text. Problems arise from the extensive nature of the XMI, the context limitation and the unique identifiers of the UML elements. The fine-tuning process enabled the LLM to gain valuable insights into UML modeling while transferring their base knowledge, which is a promising step toward reducing complexity in MBSE.