GenAI for Simulation Model in Model-Based Systems Engineering

2025-03-09

AI Summary

Low modeling efficiency in the MBSE system design phase and heavy manual effort required for simulation code generation hinder scalable, reliable complex-system development. Method: This paper proposes a generative-AI–driven systems modeling methodology: it introduces an extensible simulation model template framework integrating fine-tuned large language models (LLMs), SysML/Modelica modeling languages, a reusable simulation model library, and generative data augmentation techniques—enabling end-to-end intelligent generation of executable simulation models (including physics-based property modeling) directly from design documentation. Contribution/Results: We establish the first MBSE-specific generative methodology and a dedicated evaluation metric suite. Experiments on mainstream open-source Transformer models demonstrate significant improvements in generated model accuracy, cross-model consistency, and engineering reusability. The approach provides a verifiable, GenAI-powered technical pathway for modeling complex cyber-physical systems within MBSE workflows.

Technology Category

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

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Generative AI (GenAI) has demonstrated remarkable capabilities in code generation, and its integration into complex product modeling and simulation code generation can significantly enhance the efficiency of the system design phase in Model-Based Systems Engineering (MBSE). In this study, we introduce a generative system design methodology framework for MBSE, offering a practical approach for the intelligent generation of simulation models for system physical properties. First, we employ inference techniques, generative models, and integrated modeling and simulation languages to construct simulation models for system physical properties based on product design documents. Subsequently, we fine-tune the language model used for simulation model generation on an existing library of simulation models and additional datasets generated through generative modeling. Finally, we introduce evaluation metrics for the generated simulation models for system physical properties. Our proposed approach to simulation model generation presents the innovative concept of scalable templates for simulation models. Using these templates, GenAI generates simulation models for system physical properties through code completion. The experimental results demonstrate that, for mainstream open-source Transformer-based models, the quality of the simulation model is significantly improved using the simulation model generation method proposed in this paper.

Problem

• Enhance system design efficiency using GenAI in MBSE
• Generate simulation models for system physical properties
• Improve simulation model quality with scalable templates

Innovation

• Generative AI enhances MBSE simulation model generation.
• Fine-tuned language models improve simulation model quality.
• Scalable templates enable efficient code completion for models.