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.

提出了一个用于描述数字孪生中人工智能流程的领域特定语言（DSL），旨在简化数字孪生的设计和验证，通过将函数作为一等公民进行学习模型的有效操作，展示了 Function+Data Flow（FDF）在结构的塑性应变预测和轴承的电磁行为建模两个具体用例中的优势。

功能和数据流：用于数字双胞胎的指定机器学习流水线的框架

Function+Data Flow: A Framework to Specify Machine Learning Pipelines  for Digital Twinning

Artificial intelligence (AI), especially deep learning, requires vast amounts
of data for training, testing, and validation. Collecting these data and the
corresponding annotations requires the implementation of imaging biobanks that
provide access to these data in a standardized way. This requires careful
design and implementation based on the current standards and guidelines and
complying with the current legal restrictions. However, the realization of
proper imaging data collections is not sufficient to train, validate and deploy
AI as resource demands are high and require a careful hybrid implementation of
AI pipelines both on-premise and in the cloud. This chapter aims to help the
reader when technical considerations have to be made about the AI environment
by providing a technical background of different concepts and implementation
aspects involved in data storage, cloud usage, and AI pipelines.

这篇论文介绍了人工智能，特别是深度学习对大量数据进行训练，测试和验证的需求，以及在对数据进行标注方面实现图像生物库的重要性，并讲述了在实现合适的图像数据集合时，需要考虑法律局限和云计算等多方面，最终提出了一种基于混合云实现 AI pipelines 的解决方案。