Problem
Research questions and friction points this paper is trying to address.
Digital Twins
Machine Learning Pipelines
Pipeline Reusability
Tooling
Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines
🤖 AI Summary
研究使用Function+Data Flow及DesCartes Builder解决数字孪生开发中AI管道难以指定、验证和复用的问题，提出H-FDF扩展以支持更复杂的管道。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Function+Data Flow
DesCartes Builder
Hierarchical FDF
digital twins
machine learning pipelines
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