.png){kind=icon}
.png){kind=icon}
Return
Artificial Intelligence Enabled Systems Engineering Modeling With Retrieval Augmented Generation
.png){kind=icon}
PRE
AI
DOI：10.1002/sys.70032.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work presents an AI-enabled tool aimed at streamlining model-based systems engineering (MBSE) workflows. The tool converts natural language inputs into MBSE models by combining large language models, natural language processing techniques, and retrieval augmented generation with MBSE software APIs. Integrating generative AI into systems engineering processes is highly effective for automating routine tasks, boosting productivity, and supporting the ongoing digital transformation in the field.Summary This work enhances systems engineering processes by automating modeling tasks with an AI-driven tool. For researchers, the tool offers the ability to integrate large language models (LLMs) with model-based systems engineering (MBSE) tools through application programming interfaces (APIs). The tool's core capabilities include the automatic creation of SysML components like block definition diagrams and state machine diagrams, as well as reading and analyzing the models. With the addition of retrieval augmented generation (RAG), the program can retrieve context from documents that contain domain-specific information, which is used to improve the language model's response. For practitioners, the tool assists systems engineers with an interactive user interface that offers AI-driven model management. The tool is presented as a chatbot where users can request model updates, ask questions, or automate tasks. These abilities improve speed and accuracy within the systems engineering workflows.
Keywords:
artificial intelligence
large language model
model based systems engineering
retrieval augmented generation
SysML