Disruption forecasting at JET using neural networks
Open accessAbstract
Neural networks are trained to evaluate the risk of plasma disruptions in a tokamak experiment using several diagnostic signals as inputs. A saliency analysis confirms the goodness of the chosen inputs, all of which contribute to the network performance. Tests that were carried out refer to data collected from succesfully terminated and disruption terminated pulses performed during two years of JET tokamak experiments. Results show the possibility of developing a neural network predictor that intervenes well in advance in order to avoid plasma disruption or mitigate its effects.
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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 [110]
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Adaptive anomaly detection disruption prediction starting from first discharge on tokamak
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Vi-DP: low-latency video-based disruption prediction with multi-FOV fusion in EAST
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- Authors
- Cannas, B., Fanni, A., Marongiu, E., & Sonato, P.
- Affiliations
- University of Cagliari (IT)
- National Agency for New Technologies, Energy and Sustainable Economic Development (IT)
- Venue
- Nuclear Fusion
- Publisher
- IOP Publishing
- Published
- 2003-12-05
- Volume
- 44
- Issue
- 1
- Pages
- 68-76
- ISSN
- 0029-5515 (ISSN-L)
- 1741-4326
- OpenAlex
- W1964616952
- Cited by
- 98
- References
- 14
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 3 · 2023: 10 · 2022: 7 · 2021: 6 · 2020: 8 · 2019: 6 · 2018: 7 · 2017: 2 · 2016: 3 · 2015: 4 · 2014: 5 · 2013: 5 · 2012: 2
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nuclear Fusion (publishedVersion)
- UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- U.S. Department of Defense
- EUROfusion
- 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
- plasma disruption prediction, tokamak, JET tokamak, artificial neural networks, saliency analysis, diagnostic signals, disruption mitigation, fusion plasma
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