Predicting disruptive instabilities in controlled fusion plasmas through deep learning
Open accessAbstract
Nuclear fusion power delivered by magnetic-confinement tokamak reactors holds the promise of sustainable and clean energy 1. The avoidance of large-scale plasma instabilities called disruptions within these reactors 2,3 is one of the most pressing challenges 4,5, because disruptions can halt power production and damage key components. Disruptions are particularly harmful for large burning-plasma systems such as the multibillion-dollar International Thermonuclear Experimental Reactor (ITER) project 6 currently under construction, which aims to be the first reactor that produces more power from fusion than is injected to heat the plasma. Here we present a method based on deep learning for forecasting disruptions. Our method extends considerably the capabilities of previous strategies such as first-principles-based 5 and classical machine-learning 7-11 approaches. In particular, it delivers reliable predictions for machines other than the one on which it was trained-a crucial requirement for future large reactors that cannot afford training disruptions. Our approach takes advantage of high-dimensional training data to boost predictive performance while also engaging supercomputing resources at the largest scale to improve accuracy and speed. Trained on experimental data from the largest tokamaks in the United States (DIII-D 12) and the world (Joint European Torus, JET 13), our method can also be applied to specific tasks such as prediction with long warning times: this opens up the possibility of moving from passive disruption prediction to active reactor control and optimization. These initial results illustrate the potential for deep learning to accelerate progress in fusion-energy science and, more generally, in the understanding and prediction of complex physical systems.
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Recent work has shown that deep learning may enable insights that generalize to multiple reactors.
“recent work has shown that deep learning may enable insights that generalize to multiple reactors [413].”
From Tackling climate change with machine learning (2023) · cites this paper as [413]
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 [413]
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- Authors
- Kates-Harbeck, J., Svyatkovskiy, A., & Tang, W.
- Affiliations
- Microsoft (United States) (US)
- Princeton University (US)
- ORCID
- Tang, William
- Venue
- Nature
- Published
- 2019-04
- Volume
- 568
- Issue
- 7753
- Pages
- 526-531
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2937394206
- Cited by
- 367
- References
- 52
- Citations by year
- 2026: 28 · 2025: 64 · 2024: 54 · 2023: 57 · 2022: 49 · 2021: 60 · 2020: 42 · 2019: 13
- FWCI
- 20.84
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- U.S. Department of Energy (DEFG0297ER25308, DE‐FG02‐, 2014-2018, FG02-97ER25308, 633053, DEFC0204ER54698)
- Princeton University
- EUROfusion (training programme 2014-2018, 633053)
- European Commission (633053)
- Vedecká Grantová Agentúra MŠVVaŠ SR a SAV
- Office of Science (633053, DEFG0297ER25308)
- National Nuclear Security Administration (DE-FG02-97ER25308)
- Euratom Research and Training Programme (2014-2018, 633053)
- Fusion Energy Sciences (633053)
- 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
- disruption prediction, tokamak plasma, magnetic confinement fusion, deep neural network, plasma instabilities, DIII-D tokamak, jets, ITER
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