Applications of Artificial Intelligence to Model Based Systems Engineering: Initial Case Studies

This preliminary work explores the integration of artificial intelligence (AI) into Model Based Systems Engineering (MBSE) to enhance systems engineering processes. Through a series of initial case studies, the work examines the application of AI technologies, such as Computer Vision and Large Language Models, in areas like architecture evaluation, model creation, and requirements analysis. The work highlights the potential for AI to improve efficiency and accuracy in MBSE tasks, while also identifying challenges related to model complexity and semantic precision. It mentions several challenges, particularly regarding the ambiguity and variations inherent to large language models (LLMs), which can lead to misinterpretation of user intent, especially with complex system concepts. The paper concludes by discussing the implications of these advancements for the future of systems engineering, suggesting that AI could potentially streamline workflows and broaden accessibility within the field, provided that other limitations are addressed.