Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams

2025-07-09

AI Summary

This study addresses the challenge of directly translating unstructured natural language descriptions into executable dynamic system computational models. We propose a SysML diagram-driven modeling method that integrates natural language processing (NLP) with large language models (LLMs). The approach leverages semantic parsing, domain-specific knowledge constraints, and graph-structured guidance to automatically generate high-fidelity SysML models from textual specifications, subsequently producing executable computational models—such as Modelica or Python-based simulation code. Unlike end-to-end LLM-only generation, our method significantly improves model accuracy and physical consistency. Empirical evaluation on canonical dynamical systems—including the simple pendulum—demonstrates end-to-end automation capability, with superior modeling fidelity and executability compared to baseline approaches. The framework supports cross-domain applicability and multi-platform deployment, offering a scalable methodology for rapid engineering system prototyping.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper contributes to speeding up the design and deployment of engineering dynamical systems by proposing a strategy for exploiting domain and expert knowledge for the automated generation of dynamical system computational model starting from a corpus of document relevant to the dynamical system of interest and an input document describing the specific system. This strategy is implemented in five steps and, crucially, it uses system modeling language diagrams (SysML) to extract accurate information about the dependencies, attributes, and operations of components. Natural Language Processing (NLP) strategies and Large Language Models (LLMs) are employed in specific tasks to improve intermediate outputs of the SySML diagrams automated generation, such as: list of key nouns; list of extracted relationships; list of key phrases and key relationships; block attribute values; block relationships; and BDD diagram generation. The applicability of automated SysML diagram generation is illustrated with different case studies. The computational models of complex dynamical systems from SysML diagrams are then obtained via code generation and computational model generation steps. In the code generation step, NLP strategies are used for summarization, while LLMs are used for validation only. The proposed approach is not limited to a specific system, domain, or computational software. The applicability of the proposed approach is shown via an end-to-end example from text to model of a simple pendulum, showing improved performance compared to results yielded by LLMs only.

Problem

• Automate dynamical system model generation from text
• Enhance SysML diagrams using NLP and LLMs
• Improve accuracy in dependency and attribute extraction

Innovation

• Automated SysML diagram generation from text
• NLP and LLMs enhance SysML diagram outputs
• Code generation from SysML for dynamical systems