arXiv:2503.06422cs.SEcs.AI2025-03被引 2

用生成式AI自动生成系统仿真模型,提升设计效率

GenAI for Simulation Model in Model-Based Systems Engineering

  • 基于设计文档和生成模型,自动构建系统物理特性仿真模型
  • 通过微调语言模型,使主流Transformer模型生成质量显著提升
  • 提出可扩展的仿真模板框架,适合系统工程与仿真开发者

生成式人工智能(GenAI)在代码生成方面表现出色,将其融入复杂产品建模与仿真代码生成,可显著提升模型驱动系统工程(MBSE)中系统设计阶段的效率。本文提出一种面向MBSE的生成式系统设计方法框架,提供智能生成系统物理特性仿真模型的实用路径。首先,利用推理技术、生成模型及集成建模与仿真语言,根据产品设计文档构建系统物理特性的仿真模型;其次,在现有仿真模型库及生成模型产生的额外数据集上,对用于仿真模型生成的语言模型进行微调;最后,引入评估指标以衡量生成仿真模型的质量。所提方法创新性地提出仿真模型的可扩展模板概念,通过代码补全方式实现生成。实验结果表明,对于主流开源Transformer-based模型,该方法显著提升了仿真模型的质量。

原文摘要 · Abstract (English)

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.

生成式AI系统工程仿真建模代码生成

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