The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Tokamak disruption alarm based on a neural network model of the high- beta limit
Open accessAbstract
An artificial neural network, combining signals from a large number of plasma diagnostics, was used to estimate the high- beta disruption boundary in the DIII-D tokamak. It is shown that inclusion of many diagnostic measurements results in a much more accurate prediction of the disruption boundary than that provided by the traditional Troyon limit. A trained neural network constitutes a non-linear, non-parametric model of the disruption boundary. Through the analysis of the input-output sensitivities, the relative statistical significance of various diagnostic measurements (plasma parameters) for the determination of the disruption boundary is directly assessed and the number of diagnostics used by the neural network model is reduced to the necessary minimum. The neural network is trained to map the disruption boundary throughout most of the discharge. As a result, it can predict the high- beta disruption boundary on a time-scale of the order of 100 ms (much longer than the precursor growth time), which makes this approach ideally suitable for real time application in a disruption avoidance scheme. Owing to the relative simplicity of the required computations, the neural network is easily implemented in a real time system. A prototype of the neural network disruption alarm was installed within the DIII-D digital plasma control system, and its real time operation, with a typical time resolution of 10 ms, was demonstrated
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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 [850]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Reconstruction of current profile parameters and plasma shapes in tokamaks
Lao, L. L., John, H. S., Stambaugh, R. D., Kellman, A. G., & Pfeiffer, W. (1985) · Nuclear Fusion · doi:10.1088/0029-5515/25/11/007 · Cited by 1,794
Neural networks and their applications
Bishop, C. M. (1994) · Review of Scientific Instruments · doi:10.1063/1.1144830 · Cited by 981
Artificial neural systems: foundations, paradigms, applications, and implementations
(1990) · Choice Reviews Online · doi:10.5860/choice.28-1599 · Cited by 414
Schüller, F. C. (1995) · Plasma Physics and Controlled Fusion · doi:10.1088/0741-3335/37/11a/009 · Cited by 263
Locked modes in DIII-D and a method for prevention of the low density mode
Scoville, J. T., Haye, R. J. L., Kellman, A. G., Osborne, T. H., Stambaugh, R. D., Strait, E. J., & Taylor, T. S. (1991) · Nuclear Fusion · doi:10.1088/0029-5515/31/5/006 · Cited by 168
Stability of high beta tokamak plasmas*
Strait, E. J. (1994) · Physics of Plasmas · doi:10.1063/1.870691 · Cited by 157
Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping
Lister, J. B., & Schnurrenberger, H. (1991) · Nuclear Fusion · doi:10.1088/0029-5515/31/7/005 · Cited by 87
Feedback stabilization of disruption precursors in a tokamak
Morris, A. W. L., Hender, T. C., Hugill, J., Haynes, P. S., Johnson, P. C., Lloyd, B., Robinson, D. C., Silvester, C., Arshad, S., & Fishpool, G. (1990) · Physical Review Letters · doi:10.1103/physrevlett.64.1254 · Cited by 83
Impurity effects on imode stability and transport
Fröjdh, M., Liljeström, M., & Nordman, H. (1992) · Nuclear Fusion · doi:10.1088/0029-5515/32/3/i06 · Cited by 74
Cited by
Sorted by citations
Items 101–133 of 133
Real-Time Estimation of Vertical Instability Growth Rate for EAST Plasma With MLP
Liu, B. N., Hu, W., Huang, Y., Luo, Z., Wang, Y., Yuan, Q., Zhang, R., & Xiao, B. (2023) · IEEE Transactions on Plasma Science · doi:10.1109/tps.2023.3321377 · Cited by 3
Two-dimensional vacuum ultraviolet images in different MHD events on the EAST tokamak
Zhijun, W. A. N. G., Gao, X., Ming, T., Wang, Y., Zhou, F., Long, F., Zhuang, Q., & EAST Team (2018) · Plasma Science and Technology · doi:10.1088/2058-6272/aa9477 · Cited by 3
Luxon, J. L. (1997) · Elsevier eBooks · doi:10.1016/b978-0-444-82762-3.50028-8 · Cited by 3
Using LSTM for the Prediction of Disruption in ADITYA TokamakPreprint
Agarwal, A., Mishra, A., Sharma, P., Jain, S., Ranjan, S., & Manchanda, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.06230 · Cited by 5
张, H. 瀚. Z., 周, L. 利. Z., Liu, Y., Hao, G., 王, S. 硕. W., 杨, X. 旭. Y., Miao, Y., Duan, P., & 陈, L. 龙. C. (2023) · Plasma Science and Technology · doi:10.1088/2058-6272/ad13e3 · Cited by 2
Large-scale tearing-mode hazard function analysis with standard matched equilibrium reconstructions
Olofsson, E., Akcay, C., Sun, X., Sammuli, B., & Nazikian, R. (2025) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ade009 · Cited by 2
Craciunescu, T., Murari, A., Contributors, O. B. O. J., & the EUROfusion Tokamak Exploitation Team (2024) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ad670a · Cited by 2
Craciunescu, T., Murari, A., Tiseanu, I., & Vega, J. (2014) · IEEE Transactions on Plasma Science · doi:10.1109/tps.2014.2331705 · Cited by 2
On L to H-mode transitions of the tokamak and entropy reduction
Rastović, D. (2006) · Nuclear Technology and Radiation Protection · doi:10.2298/ntrp0602014r · Cited by 2
Adaptive anomaly detection disruption prediction starting from first discharge on tokamak
Ai, X., Zheng, W., Zhang, M., Ding, Y. H., Chen, D., Chen, Z., Guo, B., Shen, C., Wang, N., Yang, Z., et al. (2025) · Nuclear Fusion · doi:10.1088/1741-4326/ada9a9 · Cited by 1
Oh, S., Jang, J., & Peterson, B. J. (2020) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ab6b4b · Cited by 2
Hender, T. C. (2014) · Springer series on atomic, optical, and plasma physics · doi:10.1007/978-3-662-44222-7_7 · Cited by 1
Abbasi, S., Mlynar, J., Chlum, J., Ficker, O., Svoboda, V., & Brotánková, J. (2024) · Journal of Fusion Energy · doi:10.1007/s10894-024-00458-z · Cited by 1
Automated signal classification in the C-2W fusion experiment
Bolte, N. G., Allfrey, I., Mendoza, R., & TAE Team (2021) · Review of Scientific Instruments · doi:10.1063/5.0043820 · Cited by 1
Alavi, A., Saadat, S., Ghanbari, M. R., Alavi, S. E., & Kadkhodaie, A. (2021) · Physica Scripta · doi:10.1088/1402-4896/ac33f7 · Cited by 1
Kellman, A. G., Cuthbertson, J. W., Luckhardt, S., Zhang, J., Lee, R. L., Leuer, J. A., Humphreys, D. L., Hyatt, A. W., Jahns, G. L., Wróblewski, D., et al. (1997) · University of North Texas Digital Library (University of North Texas)
Self-citationRoshani, G. H., Habibi, M., Sadighzadeh, A., Afsharmanesh, M., & Rezaei, A. (2015) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3969493
Applications of Neural Networks to Data Analysis and Control of Fusion Plasma.
Takeda, T., & Iyomasa, A. (2002) · プラズマ・核融合学会誌 · doi:10.1585/jspf.78.842
Cannas, B., Delogu, R. S., Fanni, A., Montisci, A., Sonato, P., & Zedda, M. K. (2007) · Machine learning for signal processing ... · doi:10.1109/mlsp.2007.4414342
Walker, M. L., Humphreys, D. A., Mazon, D., Moreau, D., Okabayashi, M., Osborne, T. H., & Schuster, E. (2006)
Real Time Neural Network Disruption Prediction in Tokamak Reactors
Morabito, F. C., & Versaci, M. (1999) · Perspectives in neural computing · doi:10.1007/978-1-4471-0877-1_39
Horton, L. D., & Contributors, J. (2016) · Elsevier eBooks · doi:10.1016/b978-0-08-100315-2.00008-8
Wolf, G. H. (1998) · Fusion Technology · doi:10.13182/fst98-a11947028
Paluszek, M., & Thomas, S. J. (2020) · Apress eBooks · doi:10.1007/978-1-4842-5124-9_6
Plasma Control Concepts for ITER
Lister, J. B. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5353-3_7
Paluszek, M., Thomas, S. J., & Ham, E. (2022) · Apress eBooks · doi:10.1007/978-1-4842-7912-0_6
Li, Z., Ye, Y., Kong, D., Tan, M., Zhong, F., Wang, M., Qu, C., Zhao, Z., Wu, Y., Zhang, Q., et al. (2024) · Review of Scientific Instruments · doi:10.1063/5.0230459
Joshi, R. C. (2025) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn67655.2025.11368018
Machine Learning Applied to Problems of Nuclear Fusion
Chetkin, E. I., & Shishkin, A. V. (2025) · Physics of Atomic Nuclei · doi:10.1134/s1063778825140017
Yang, X., Yuan, J., & Yang, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6275396
Yang, X., Yuan, J., Yang, J., Liu, Y., He, X., Zhang, Q., Liu, C., Dong, G., & He, X. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115512
Wang, Y., Yang, J., Ren, Z., Kuang, J., Fang, J., Li, Z., Chen, H., & Wang, W. (2026) · Physica Scripta · doi:10.1088/1402-4896/ae9e95
Metadata
- Authors
- Wroblewski, D., Jahns, G. L., & Leuer, J. A.
- Affiliations
- Lockheed Martin Orincon (United States) (US)
- ORCID
- Leuer, J.A
- Venue
- Nuclear Fusion
- Publisher
- IOP Publishing
- Published
- 1997-06
- Volume
- 37
- Issue
- 6
- Pages
- 725-741
- ISSN
- 0029-5515 (ISSN-L)
- OpenAlex
- W2014096208
- Cited by
- 135
- References
- 22
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 6 · 2023: 8 · 2022: 5 · 2021: 14 · 2020: 17 · 2019: 7 · 2018: 5 · 2017: 1 · 2016: 4 · 2015: 3 · 2014: 3 · 2013: 3
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nuclear Fusion (publishedVersion)
- Funders
- U.S. Department of Energy
- Small Business Innovation Research
- Princeton Plasma Physics Laboratory
- 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
- tokamak disruptions, artificial neural networks, DIII-D tokamak, plasma diagnostics, disruption avoidance, plasma control system
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
