A cross-tokamak neural network disruption predictor for the JET and ASDEX Upgrade tokamaks
Open accessAbstract
First results are reported on the prediction of disruptions in one tokamak, based on neural networks trained on another tokamak. The studies use data from the JET and ASDEX Upgrade devices, with a neural network trained on just seven normalized plasma parameters. In this way, a simple single layer perceptron network trained solely on JET correctly anticipated 67% of disruptions on ASDEX Upgrade in advance of 0.01 s before the disruption. The converse test led to a 69% success rate in advance of 0.04 s before the disruption in JET. Only one overall time scaling parameter is allowed between the devices, which can be introduced from theoretical arguments. Disruption prediction performance based on such networks trained and tested on the same device shows even higher success rates (JET, 86%; ASDEX Upgrade, 90%), despite the small number of inputs used and simplicity of the network. It is found that while performance for networks trained and tested on the same device can be improved with more complex networks and many adjustable weights, for cross-machine testing the best approach is a simple single layer perceptron. This offers the basis of a potentially useful technique for large future devices such as ITER, which with further development might help to reduce disruption frequency and minimize the need for a large disruption campaign to train disruption avoidance systems.
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Prior work has tried to preemptively detect disruptions for tokamak reactors, using supervised learning methods such as support vector machines, adaptive fuzzy logic, decision trees, and deep learning on previous disruption data.
“Prior work has tried to preemptively detect disruptions for tokamak reactors, using supervised learning methods such as supportvector machines, adaptive fuzzy logic, decision trees, and deep learning [110,413,567,811,846,850] on previous disruption data.”
From Tackling climate change with machine learning (2023) · cites this paper as [846]
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On-the-fly training architecture for a time-series neural network on ADITYA/ADITYA-U data
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New information processing methods for control on JET
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- Authors
- Windsor, C. G., Pautasso, G., Tichmann, C., Buttery, R. J., Hender, T. C., Contributors, J. E., & Team, T. A. U.
- Affiliations
- Culham Centre for Fusion Energy (GB)
- Max Planck Society (DE)
- Venue
- Nuclear Fusion
- Publisher
- IOP Publishing
- Published
- 2005-04-26
- Volume
- 45
- Issue
- 5
- Pages
- 337-350
- ISSN
- 0029-5515 (ISSN-L)
- 1741-4326
- OpenAlex
- W2050413758
- Cited by
- 91
- References
- 14
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 9 · 2023: 10 · 2022: 4 · 2021: 8 · 2020: 10 · 2019: 11 · 2018: 5 · 2017: 1 · 2016: 1 · 2014: 6 · 2013: 3 · 2012: 2
- FWCI
- 0.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nuclear Fusion (publishedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- EUROfusion
- Engineering and Physical Sciences Research Council
- Topics
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Magnetic confinement fusion research
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Fusion and Plasma Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Fusion materials and technologies
- Keywords
- single-layer perceptron, JET tokamak, ASDEX Upgrade, plasma parameters, disruption avoidance, ITER
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