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AI-augmented systems engineering: conceptual application of retrieval-augmented generation for model-based systems engineering graph
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ABSTRACT:This paper presents the MBSE-Graph-RAG framework to address key challenges in Model-Based Systems Engineering (MBSE). Traditional MBSE tools suffer from usability barriers, limited accessibility, and integration challenges. By combining knowledge graphs with Retrieval-Augmented Generation (RAG), the proposed framework enables AI-Augmented engineering through natural language interactions and automated system architecture generation. A systematic literature review establishes a solid research foundation, identifying gaps in AI-assisted MBSE. Key contributions include a structured MBSE-Graph interface, improved usability via Large Language Models (LLMs), and automated graph construction aligned with SysML. A proof-of-concept demonstrates the potential of this approach to enhance MBSE by reducing complexity, improving data accessibility, and supporting engineering collaboration.
Cambridge University Press (CUP)
Title: AI-augmented systems engineering: conceptual application of retrieval-augmented generation for model-based systems engineering graph
Description:
ABSTRACT:This paper presents the MBSE-Graph-RAG framework to address key challenges in Model-Based Systems Engineering (MBSE).
Traditional MBSE tools suffer from usability barriers, limited accessibility, and integration challenges.
By combining knowledge graphs with Retrieval-Augmented Generation (RAG), the proposed framework enables AI-Augmented engineering through natural language interactions and automated system architecture generation.
A systematic literature review establishes a solid research foundation, identifying gaps in AI-assisted MBSE.
Key contributions include a structured MBSE-Graph interface, improved usability via Large Language Models (LLMs), and automated graph construction aligned with SysML.
A proof-of-concept demonstrates the potential of this approach to enhance MBSE by reducing complexity, improving data accessibility, and supporting engineering collaboration.
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