Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909245426892800 |
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| author | de Conto, Eduardo Genest, Blaise Easwaran, Arvind |
| author_facet | de Conto, Eduardo Genest, Blaise Easwaran, Arvind |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_19670 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning de Conto, Eduardo Genest, Blaise Easwaran, Arvind Software Engineering Artificial Intelligence Machine Learning 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. |
| title | Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.19670 |