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  • presenting Pinboard papers under review for journal or conference publication,
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  • presenting work done in PhD projects funded by or co-supervised by UKAEA,
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If you would like to invite a speaker on a topic that would be of interest to one or more Units within the Computing Division, but is not currently collaborating on a UKAEA project, please consider nominating them for a Computing Division Cross-Disciplinary Seminar.

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CD Technical Meetings Archive on UKAEA Sharepoint

CD Technical Meeting (HD1): Towards Open Machine Learning Datasets for Fusion with AI Driven Annotation & Visualization

→ Europe/London
Aravinda Perera (Plasma Simulation)
Description

Abstract

 

Machine learning (ML) has become a powerful tool for aiding and enhancing data analysis in plasma science[1], presenting exciting opportunities to automate workflows and accelerate scientific discovery. However, machine learning is famously data hungry, requiring costly annotation and curation, which is exacerbated by the specialist knowledge & expertise required within the fusion domain. Furthermore, the general lack of open and widely accessible fusion diagnostic data for benchmarking the ML models chronically hinders development and comparison across experimental facilities. 

The FAIR-MAST[2, 3] project at the UKAEA aimed to curate and release an extensive, open catalog of historical diagnostic data from the MAST machine following FAIR[4] principles. However, high quality associated metadata for describing 1) key plasma events occurring within each discharge (disruptions, confinement mode, ELMs, MHD modes, sawteeth, UFOs etc.), and 2) the validity of the diagnostic data is lacking. Such historical metadata is not only crucial for efficient user search and retrieval but is also fundamental towards automating analysis and scientific workflows at experimental facilities. Furthermore, acquiring such a collection of metadata on the MAST machine would provide a valuable open data for model comparison within the fusion domain. 

To address these challenges, we present progress on the development of an AI driven, human-in-the-loop web UI platform for the annotation and curation of diagnostic data, enabling us to bring together domain expertise with AI tools to accelerate and automate its collection. Crucially, our platform provides a framework to plug in custom models for automatically annotating regions of interest within diagnostic data traces. Annotation models could be anything between classical signal processing to ML models. Our platform is built on a modern web stack and fully open source. The platform is designed to be machine agnostic and extensible, supporting a wide variety of data access layers, including both domain specific and general formats. Metadata captured by the platform is stored in a machine-readable database accessible through a REST API.  

We will discuss future plans for the development of a public database of annotated data and downstream tasks towards providing the community with a dataset for model intercomparison within the FAIR MAST project as well as how we may “close the loop” on the annotator training. 

 

References 

[1] R. Anirudh, et al, IEEE Trans. Plasma. Sci., doi: 10.1109/TPS.2023.3268170 (2022)  

[2] S. Jackson et al, IEEE Trans. Plasma. Sci., doi: 10.1109/TPS.2025.3583419 (2025) 

[3] S. Jackson et al, Software X, doi: 10.1016/j.softx.2024.101869 (2024) 

[4] Wilkinson, et al. Nature Sci. Data, doi:  10.1038/sdata.2016.18 

Participants
  • Jony Castagna