Results of the JET real-time disruption predictor in the ITER-like wall campaigns
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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 [811]
Cites
Cherkassky, V., & Mulier, F. M. (1998) · Cited by 384 · see citing works
Survey of disruption causes at JET
de Vries, P. C., Johnson, M. F., Alper, B., Buratti, P., Hender, T. C., Koslowski, H. R., & Riccardo, V. (2011) · Nuclear Fusion · doi:10.1088/0029-5515/51/5/053018 · Cited by 262 · see citing works
MARTe: A Multiplatform Real-Time Framework
Neto, A. C., Sartori, F., Piccolo, F., et al. (2010) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2009.2037815 · Cited by 159 · see citing works
An advanced disruption predictor for JET tested in a simulated real-time environment
Ratta, G. A., Vega, J., Murari, A., Vagliasindi, G., Johnson, M. F., & de Vries, P. C. (2010) · Nuclear Fusion · doi:10.1088/0029-5515/50/2/025005 · Cited by 123 · see citing works
Self-citationImproved feature selection based on genetic algorithms for real time disruption prediction on JET
Ratta, G. A., Vega, J., & Murari, A. (2012) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2012.07.002 · Cited by 38 · see citing works
Self-citationFeature extraction for improved disruption prediction analysis at JET
Ratta, G. A., Vega, J., Murari, A., Johnson, M. F., & Contributors, J.-E. (2008) · Review of Scientific Instruments · doi:10.1063/1.2965020 · Cited by 26 · see citing works
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Predicting disruptive instabilities in controlled fusion plasmas through deep learning
Kates-Harbeck, J., Svyatkovskiy, A., & Tang, W. (2019) · Nature · doi:10.1038/s41586-019-1116-4 · Cited by 367 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Vega, J., Dormido-Canto, S., López, J. M., Murari, A., Ramírez, J. M., Moreno, R., Ruiz, M., Alves, D., & Felton, R.
- Affiliations
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES)
- Universidad Nacional de Educación a Distancia (ES)
- Consejo Superior de Investigaciones Científicas (ES)
- National Agency for New Technologies, Energy and Sustainable Economic Development (IT)
- Instituto Superior Técnico (PT)
- Culham Centre for Fusion Energy (GB)
- Corresponding author
- Vega, Jesús
- ORCID
- Venue
- Fusion Engineering and Design
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 88
- Issue
- 6-8
- Pages
- 1228-1231
- ISSN
- 0920-3796 (ISSN-L)
- OpenAlex
- W2029148907
- Cited by
- 104
- References
- 6
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 14 · 2023: 6 · 2022: 6 · 2021: 5 · 2020: 15 · 2019: 10 · 2018: 9 · 2017: 5 · 2016: 6 · 2015: 6 · 2014: 9 · 2013: 2
- FWCI
- 3.50
- Type
- journal article
- Language
- en
- Open access
- green
- oa.upm.es
- Licence
- Locations
- Fusion Engineering and Design (publishedVersion)
- UPM Digital Archive (Technical University of Madrid) (acceptedVersion, cc-by-nc-nd)
- Funders
- European Commission
- Culham Centre for Fusion Energy
- Engineering and Physical Sciences Research Council (EP/I501045/1)
- 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
- JET tokamak, ITER-like wall, disruption mitigation, false alarm rate, missed alarm rate, predictive machine learning, fusion plasma
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