The Data Fusion Labeler (dFL): Challenges and Solutions to Data Harmonization, Labeling, and Provenance in Fusion Energy
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866912705429897216 |
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| author | Michoski, Craig Waller, Matthew Sammuli, Brian Li, Zeyu Nakkina, Tapan Ganatma Nazikian, Raffi Smith, Sterling Orozco, David Kuang, Dongyang Foltin, Martin Olofsson, Erik Fredrickson, Mike Louis-Jeune, Jerry Hatch, David R. Oliver, Todd A. Clark, Mitchell Louis, Steph-Yves |
| author_facet | Michoski, Craig Waller, Matthew Sammuli, Brian Li, Zeyu Nakkina, Tapan Ganatma Nazikian, Raffi Smith, Sterling Orozco, David Kuang, Dongyang Foltin, Martin Olofsson, Erik Fredrickson, Mike Louis-Jeune, Jerry Hatch, David R. Oliver, Todd A. Clark, Mitchell Louis, Steph-Yves |
| contents | Fusion energy research increasingly depends on the ability to integrate heterogeneous, multimodal datasets from high-resolution diagnostics, control systems, and multiscale simulations. The sheer volume and complexity of these datasets demand the development of new tools capable of systematically harmonizing and extracting knowledge across diverse modalities. The Data Fusion Labeler (dFL) is introduced as a unified workflow instrument that performs uncertainty-aware data harmonization, schema-compliant data fusion, and provenance-rich manual and automated labeling at scale. By embedding alignment, normalization, and labeling within a reproducible, operator-order-aware framework, dFL reduces time-to-analysis by greater than 50X (e.g., enabling >200 shots/hour to be consistently labeled rather than a handful per day), enhances label (and subsequently training) quality, and enables cross-device comparability. Case studies from DIII-D demonstrate its application to automated ELM detection and confinement regime classification, illustrating its potential as a core component of data-driven discovery, model validation, and real-time control in future burning plasma devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09725 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Data Fusion Labeler (dFL): Challenges and Solutions to Data Harmonization, Labeling, and Provenance in Fusion Energy Michoski, Craig Waller, Matthew Sammuli, Brian Li, Zeyu Nakkina, Tapan Ganatma Nazikian, Raffi Smith, Sterling Orozco, David Kuang, Dongyang Foltin, Martin Olofsson, Erik Fredrickson, Mike Louis-Jeune, Jerry Hatch, David R. Oliver, Todd A. Clark, Mitchell Louis, Steph-Yves Plasma Physics Machine Learning Data Analysis, Statistics and Probability I.2 Fusion energy research increasingly depends on the ability to integrate heterogeneous, multimodal datasets from high-resolution diagnostics, control systems, and multiscale simulations. The sheer volume and complexity of these datasets demand the development of new tools capable of systematically harmonizing and extracting knowledge across diverse modalities. The Data Fusion Labeler (dFL) is introduced as a unified workflow instrument that performs uncertainty-aware data harmonization, schema-compliant data fusion, and provenance-rich manual and automated labeling at scale. By embedding alignment, normalization, and labeling within a reproducible, operator-order-aware framework, dFL reduces time-to-analysis by greater than 50X (e.g., enabling >200 shots/hour to be consistently labeled rather than a handful per day), enhances label (and subsequently training) quality, and enables cross-device comparability. Case studies from DIII-D demonstrate its application to automated ELM detection and confinement regime classification, illustrating its potential as a core component of data-driven discovery, model validation, and real-time control in future burning plasma devices. |
| title | The Data Fusion Labeler (dFL): Challenges and Solutions to Data Harmonization, Labeling, and Provenance in Fusion Energy |
| topic | Plasma Physics Machine Learning Data Analysis, Statistics and Probability I.2 |
| url | https://arxiv.org/abs/2511.09725 |