基于函数+数据流构建实时数字孪生实例：用户评估与迭代流水线扩展
Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines
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- Nanyang Technological University（南洋理工大学）
- CNRS（法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
该研究通过用户评估验证了函数+数据流（FDF）及DesCartes Builder可提升AI驱动数字孪生开发的易用性与可靠性，并提出其分层扩展H-FDF支持迭代模块化复杂流水线。
中文摘要 AI 辅助
数字孪生（DT）越来越多地利用人工智能（AI）和机器学习（ML）流水线，既用于从高保真模拟中构建实时数字孪生，也用于用历史数据实例化它们。然而，这些流水线的工程化在很大程度上仍是临时的：流水线难以指定、验证和复用，专用工具也十分匮乏。函数+数据流（FDF）通过定义一种可视化领域特定语言（DSL）来解决这一问题，该语言明确表示函数（ML模型），支持其组合与复用。我们在DesCartes Builder中实现了FDF，这是一个集成建模环境，支持基于FDF的数字孪生合成与验证。在本文中，我们报告了一项实证用户研究，评估FDF和DesCartes Builder能否让基于AI的数字孪生开发更易上手且更可靠。参与者在DesCartes Builder中实现了一个具有代表性的实时数字孪生原型，我们通过定量和定性指标测量了感知可用性和功能充足性。我们的结果表明，DesCartes Builder和FDF在广泛的潜在用户中实现了良好的可用性水平，尤其面向目标受众领域专家。该研究还揭示了工具和底层FDF框架的具体优势和改进领域。基于这些发现，我们提出了H-FDF，这是FDF的分层扩展，支持迭代和模块化流水线，能够对更复杂的数字孪生流水线（如双训练）进行形式化指定。我们的发现表明，集成的、模型驱动的平台是将基于AI的数字孪生工程转变为规范建模实践的有前景方向。
英文摘要
Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instantiate them with historical data. However, engineering these pipelines remains largely ad-hoc: pipelines are hard to specify, validate, and reuse, with scarce dedicated tooling. Function+Data Flow (FDF) addresses this by defining a visual domain-specific language (DSL) that represents functions (ML models) explicitly, enabling their composition and reuse. We implemented FDF in DesCartes Builder, an integrated modeling environment supporting FDF-based DT synthesis and validation. In this paper, we report on an empirical user study evaluating whether FDF and DesCartes Builder can make AI-based DT development more accessible and reliable. Participants implemented a representative real-time DT prototype within DesCartes Builder, and we measured perceived usability and feature adequacy through quantitative and qualitative measures. Our results indicate that DesCartes Builder and FDF achieve a good level of usability across a broad range of potential users, and particularly for the intended audience of domain experts. The study additionally surfaces concrete strengths and areas for improvement of both the tool and the underlying FDF framework. Informed by these findings, we propose H-FDF, a Hierarchical extension of FDF supporting iterative and modular pipelines, enabling the formal specification of more complex DT pipelines such as dual training. Our findings suggest that integrated, model-driven platforms are a promising direction to transform AI-based DT engineering into a disciplined modeling practice.
