Coincidence anomaly detection for unsupervised locating of edge localized modes in the DIII-D tokamak dataset

Abstract Using supervised learning to train a machine learning model to predict an on-coming Edge Localized Mode (ELM) requires a large number of labeled samples. Creating an appropriate data set from the very large database of discharges at a long-running tokamak, such as DIII-D, would be a very ti...

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Bibliographic Details
Published in:Machine learning: science and technology 2024-08, Vol.5 (3)
Main Authors: O’Shea, Finn H., Joung, Semin, Smith, David R., Ratner, Daniel, Coffee, Ryan
Format: Article
Language:English
Subjects:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY
anomaly detection
artificial intelligence
edge localized mode
ELM
machine learning
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Summary:Abstract Using supervised learning to train a machine learning model to predict an on-coming Edge Localized Mode (ELM) requires a large number of labeled samples. Creating an appropriate data set from the very large database of discharges at a long-running tokamak, such as DIII-D, would be a very time-consuming process for a human. Considering this need and difficulty, we use coincidence anomaly detection, an unsupervised learning technique, to train an ELM-identifier to identify and label ELMs in the DIII-D discharge database. This ELM-identifier shows, simultaneously, a precision of 0.68 and a recall of 0.63 (AUC is 0.73) on identifying ELMs in example time series pulled from thousands of discharges spanning five years. In a test set of 50 discharges, the algorithm finds over 26 thousand ELM candidates, more than 5 times the existing catalog of ELMs labeled by humans.
ISSN:2632-2153
2632-2153