GenAI for Simulation Model in Model-Based Systems Engineering
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912265806020608 |
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| author | Zhang, Lin Zhang, Yuteng Niyato, Dusit Ren, Lei Gu, Pengfei Chen, Zhen Laili, Yuanjun Cai, Wentong Bruzzone, Agostino |
| author_facet | Zhang, Lin Zhang, Yuteng Niyato, Dusit Ren, Lei Gu, Pengfei Chen, Zhen Laili, Yuanjun Cai, Wentong Bruzzone, Agostino |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06422 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GenAI for Simulation Model in Model-Based Systems Engineering Zhang, Lin Zhang, Yuteng Niyato, Dusit Ren, Lei Gu, Pengfei Chen, Zhen Laili, Yuanjun Cai, Wentong Bruzzone, Agostino Software Engineering Artificial Intelligence 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. |
| title | GenAI for Simulation Model in Model-Based Systems Engineering |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2503.06422 |