Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
<|reference_start|>Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning: 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.<|reference_end|>
@article{de conto2024function+data,
    title={Function+Data Flow: A Framework to Specify Machine Learning Pipelines
  for Digital Twinning},
    author={Eduardo de Conto, Blaise Genest, Arvind Easwaran},
    journal={arXiv preprint arXiv:2406.19670},
    year={2024},
    doi={10.1145/3664646.3664759},
    archivePrefix={arXiv},
    eprint={2406.19670},
    primaryClass={cs.SE cs.AI cs.LG}
}