The development of digital twins (DTs) for physical systems increasingly leverages artificial intelligence (AI), particularly for combining data from different sources or for creating computationally efficient, reduced-dimension models. Indeed, even in very different application domains, twinning employs common techniques such as model order reduction and modelization with hybrid data (that is, data sourced from both physics-based models and sensors). Despite this apparent generality, current development practices are ad-hoc, making the design of AI pipelines for digital twinning complex and time-consuming. Here we propose Function+Data Flow (FDF), a domain-specific language (DSL) to describe AI pipelines within DTs. FDF aims to facilitate the design and validation of digital twins. Specifically, FDF treats functions as first-class citizens, enabling effective manipulation of models learned with AI. We illustrate the benefits of FDF on two concrete use cases from different domains: predicting the plastic strain of a structure and modeling the electromagnetic behavior of a bearing.

翻译：物理系统数字孪生（DT）的开发日益借助人工智能（AI），特别是在融合多源数据或构建计算高效、降维模型方面。事实上，即便在截然不同的应用领域中，孪生技术也普遍采用诸如模型降阶和基于混合数据（即源自物理模型与传感器的数据）建模等共性方法。尽管存在这种表面上的通用性，当前的开发实践仍处于特定领域定制状态，导致数字孪生的AI流水线设计复杂且耗时。本文提出函数+数据流（FDF），这是一种用于描述DT内部AI流水线的领域特定语言（DSL）。FDF旨在简化数字孪生的设计与验证。具体而言，FDF将函数视为一等公民，从而实现对AI学习模型的有效操控。我们通过两个不同领域的具体用例——结构塑性应变预测与轴承电磁行为建模——阐释了FDF的优势。