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

• URL: http://arxiv.org/abs/2406.19670v2
• Date: Mon, 8 Jul 2024 08:28:34 GMT
• Title: Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
• Authors: Eduardo de Conto, Blaise Genest, Arvind Easwaran,
• Abstract summary: Digital twins (DTs) for physical systems increasingly leverage artificial intelligence (AI) Here we propose a domain-specific language (t+Data Flow) to describe AI pipelines within DTs. Specifically, t treats functions as first-class citizens, enabling effective manipulation of models learned with AI.
• Score: 2.27626288527213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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