U.S. Department of EnergyOffice of Scientific and Technical Information
A semi-supervised machine learning detector for physics events in tokamak discharges
Abstract
Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as themore »
- Authors:
-
- OSTI
- Publication Date:
- DOE Contract Number:
- SC0010492; FC02-04ER54698
- Research Org.:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Plasma Science and Fusion Center; General Atomics, San Diego, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Fusion Energy Sciences (FES)
- Subject:
- 70 PLASMA PHYSICS AND FUSION TECHNOLOGY
- OSTI Identifier:
- 1887434
- DOI:
- https://doi.org/10.7910/DVN/2LGWJR
Citation Formats
Montes, Kevin Joseph, Rea, Cristina, Tinguely, Roy Alexander, Sweeney, Ryan, Zhu, Jinxiang, and Granetz, Robert. A semi-supervised machine learning detector for physics events in tokamak discharges. United States: N. p., 2021.
Web. doi:10.7910/DVN/2LGWJR.
Montes, Kevin Joseph, Rea, Cristina, Tinguely, Roy Alexander, Sweeney, Ryan, Zhu, Jinxiang, & Granetz, Robert. A semi-supervised machine learning detector for physics events in tokamak discharges. United States. doi:https://doi.org/10.7910/DVN/2LGWJR
Montes, Kevin Joseph, Rea, Cristina, Tinguely, Roy Alexander, Sweeney, Ryan, Zhu, Jinxiang, and Granetz, Robert. 2021.
"A semi-supervised machine learning detector for physics events in tokamak discharges". United States. doi:https://doi.org/10.7910/DVN/2LGWJR. https://www.osti.gov/servlets/purl/1887434. Pub date:Fri Jun 25 04:00:00 UTC 2021
@article{osti_1887434,
title = {A semi-supervised machine learning detector for physics events in tokamak discharges},
author = {Montes, Kevin Joseph and Rea, Cristina and Tinguely, Roy Alexander and Sweeney, Ryan and Zhu, Jinxiang and Granetz, Robert},
abstractNote = {Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.},
doi = {10.7910/DVN/2LGWJR},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {6}
}