Cited by 367Magnetic confinement fusion researchFusion and Plasma Physics StudiesFusion materials and technologies

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Predicting disruptive instabilities in controlled fusion plasmas through deep learning

Open access

Kates-Harbeck, Julian, Svyatkovskiy, Alexey, Tang, William·2019·Nature

Open this paper in CoChat — free account Open PDF

Abstract

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.

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

Crossref · 10.1038/s41586-019-1116-4

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405

Show all 52 references

Research works printed but not linked: 1

Tang, W. et al. Scientific grand challenges: fusion energy science and the role of computing at the extreme scale (US Department of Energy’s Office of Fusion Energy Sciences, Workshop March 18–20, Washington DC, 2009).

Sorted by citations

Items 301–364 of 364

A generative artificial intelligence framework for long-time plasma turbulence simulations
Clavier, B., Zarzoso, D., del-Castillo-Negrete, D., & Frénod, E. (2025) · Physics of Plasmas · doi:10.1063/5.0255386 · Cited by 1
Symbolic dynamics for disruption prediction in case of data scarcity and diagnostic limitations
Craciunescu, T., Murari, A., Rossi, R., Vega, J., Gelfusa, M., Contributors, O. B. O. J., & the EUROfusion Tokamak Exploitation Team (2025) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/adf463 · Cited by 1
Prediction of mode structure using a novel physics-embedded neural ODE method
(朱博文), B. Z., Wang, H., Wu, J., & Ren, H. (2025) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/adfd13 · Cited by 1
Turbulence statistical analysis of the L-H transition and RMPs in KSTAR
Kim, E., Chung, H., Choi, M., Lee, Y., Han, S., Choi, G. J., Park, J.-K., Yang, S. M., & Na, Y.-S. (2025) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ae087a · Cited by 1
The Economics of Fusion: Fusion and Strategic Real Options, Knowledge Arbitrage, and Serendipity
Carayannis, E. G., Draper, J., & Erokhin, V. L. (2025) · Synthesis lectures on engineering, science, and technology · doi:10.1007/978-3-032-03127-3_11 · Cited by 1
Vi-DP: low-latency video-based disruption prediction with multi-FOV fusion in EAST
Chen, M.-W., Yang, K. J., Zhu, J., Wang, Q., Cheng, F., Chen, D., Gong, X., Zhang, B., Zhang, X. H., Duan, Y., et al. (2025) · Nuclear Fusion · doi:10.1088/1741-4326/ae15a2 · Cited by 1
HIPED: machine learning framework for spherical tokamak pedestal prediction and optimization
Parisi, J. F., Clark, J. G., Berkery, J. W., Bowman, C., Fitzpatrick, C. J., Kaye, S. M., & Lampert, M. (2025) · Nuclear Fusion · doi:10.1088/1741-4326/ae27e7 · Cited by 1
Data-driven model for predicting divertor detachment and X-point radiation distribution in EAST
Guo, B., Yu, Y., Zhang, C., Yang, Q., Meng, L. Y., Wu, K., Duan, Y., Wang, L., Sang, C., & Wang, D. (2025) · Physica Scripta · doi:10.1088/1402-4896/ae297d · Cited by 1
Machine learning surrogates for ion energy–angle distributions in thermal and RF plasma sheaths
Mustafa, M., & Curreli, D. (2026) · Journal of Plasma Physics · doi:10.1017/s0022377826101561 · Cited by 1
Disruption prediction based on fusion feature extractor on J-TEXT
Zheng, W., Xue, F., Chen, Z., Shen, C., Ai, X., Zhong, Y., Wang, N., Zhang, M., Ding, Y., Chen, Z., et al. (2023) · Chinese Physics B · doi:10.1088/1674-1056/acc7fc · Cited by 1
Reconstruction of poloidal magnetic field profiles in field-reversed configurations with machine learning in laser-driven ion-beam trace probe
徐, X. 栩. X., 徐, T. 田. X., 肖, C. 池. X., 张, Z. 祖. Z., 何, R. 任. H., 袁, R. 瑞. Y., & 许, P. 平. X. (2023) · Plasma Science and Technology · doi:10.1088/2058-6272/ad1042 · Cited by 1
Plasma current tomography for HL-2A based on Bayesian inference
刘, Z. 自. L., 王, T. 天. W., Wu, M., 罗, Z. 正. L., 王, S. 硕. W., 孙, T. 腾. S., Xiao, B., & 李, J. 建. L. (2023) · Plasma Science and Technology · doi:10.1088/2058-6272/ad1980 · Cited by 1
Deep Learning-Driven Detection of Nuclear Fusion Ignition: Illuminating the Path to Clean and Sustainable Energy
Chithambaram, T., & Purushotham, S. (2023) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon60755.2023.10442476 · Cited by 1
Estimation of Plasma Vertical Position by Long-Short Term Memory Network with Time2Vec in a Small Tokamak Device PHiX
Sejung, J. A. N. G., & Tsutsui, H. (2024) · Plasma and Fusion Research · doi:10.1585/pfr.19.1403023 · Cited by 1
The Interdependence of AI and Sustainability: Can AI Show a Path Toward Sustainability?
Schebesch, K. B. (2020) · Springer proceedings in business and economics · doi:10.1007/978-3-030-43449-6_23 · Cited by 1
DASH: Scheduling Deep Learning Workloads on Multi-Generational GPU-Accelerated Clusters
Li, B., Patel, T., Gadepally, V. N., Gettings, K., Samsi, S., & Tiwari, D. (2022) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec55821.2022.9926390 · Cited by 2
Machine Intelligence for Scientific Discovery and Engineering Invention
Daniels, M., Toney, A., Flagg, M. L., & Yang, C. (2021) · doi:10.51593/20200099 · Cited by 1
DisruptionBench: A robust benchmarking framework for machine learning-driven disruption predictionPreprint
Lucas, S., Bonotto, M., Arnold, W., Chayapathy, D., Gallingani, T., Spangher, A., Cannarile, F., Bigoni, D., Marchi, E. D., & Rea, C. (2024) · Research Square · doi:10.21203/rs.3.rs-4245117/v1 · Cited by 1
A New Pooling Method for Cnn-based Deep Learning ModelsPreprint
Turhan, K., Kalaycı, E., & Özdemir, S. (2025) · Research Square · doi:10.21203/rs.3.rs-5871802/v1 · Cited by 1
Discharge modeling in EAST using bidirectional LSTMPreprint
Wan, C., Yu, Z., Liu, X., & Li, J. (2021) · arXiv (Cornell University)
[enter Paper TitlThe Potential Role of the Artificial Intelligence in Combating Climate Change and Natural Resources Management: Political, Legal and Ethical Challengese]Preprint
Resources, G. J. O. N. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3951302
Tokamak Disruption Detection
Paluszek, M., Thomas, S. J., & Ham, E. (2022) · Apress eBooks · doi:10.1007/978-1-4842-7912-0_6
The usage of perceptron, feed and deep feed forward artificial neural networks on the spectroscopy data: astrophysical & fusion plasmas
Sakan, N. M., Traparić, I., Srécković, V. A., & Ivkovic, M. (2022) · Contributions of the Astronomical Observatory Skalnaté Pleso · doi:10.31577/caosp.2022.52.3.97 · Cited by 3
Application of Machine Learning for Oes Data in Nagdis-IiPreprint
Kajita, S., Sakakibara, T., Kaizawa, H., Natsume, H., Tanaka, H., Fujii, K., & Ohno, N. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4482191
Application of neural networks in beam emission spectroscopy modelling
Jalalvand, A., Asztalos, Ö., Karacsonyi, M., & Pokol, G. I. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10192040
How Can Artificial Intelligence Contribute to Mitigating Climate Change?
Nayak, P., Bag, A., & Maria, A. V. (2025) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3693-8684-2.ch006
Self-Attention-based Sequential Plasma Evolution Simulator for EXL-50U Tokamak
Zhao, H., Cui, C., Wang, C., Sun, T., Li, J., & Guo, D. (2024) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc64144.2024.00083
Integrated modeling and experimental validation of H-mode divertor detachment and core confinement compatibility on HL-2A tokamak
Yukun, S., Zhanhui, W. A. N. G., Xin-Liang, X., Wu, X., Wang, Z., Wu, T., Zhou, Y., Cailong, F., ZHONG, Y., Yu, X., et al. (2025) · Acta Physica Sinica · doi:10.7498/aps.74.20250087
Exploiting Memory-Aware Q-Distribution Prediction for Nuclear Fusion via Modern Hopfield Network
Ma, Q., Wang, S., Zheng, T., Dai, X., Wang, Y., Yang, Q., & Wang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-2882-7_11
Prediction of Radiative Collapse in the Large Helical Device Plasma Discharges using Convolutional Neural Networks
Suzuki, Y., Shoji, M., Kenmochi, N., & Yokoyama, M. (2025) · Plasma and Fusion Research · doi:10.1585/pfr.20.1402021
Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks
Bai, Z., Wei, X., Tang, W. M., Oliker, L., Lin, Z., & Williams, S. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/adca83
Self-citation
Making Sense of the Technical Strengths and Limitations of LLMs
Stone, M. (2024) · Critical AI · doi:10.1215/2834703x-11556020
Development of Data Assimilation System for Temperature and Density Control in Helical Plasmas
Morishita, Y., Murakami, S., Kenmochi, N., Yokoyama, M., Ueno, G., & Osakabe, M. (2025) · Plasma and Fusion Research · doi:10.1585/pfr.20.1403034
Multi-level noise reduction for 2D backlighting x-ray radiography using Frequency Residual U-Net
Hua, R., Sun, C., Sun, C., Chen, B., Miao, W., Tu, S., Chen, Z., Yuan, Y., Sun, C., & Guo, L. (2025) · Physics of Plasmas · doi:10.1063/5.0256601
Machine-learned trends in mirror configurations in the large plasma device
Travis, P., Bortnik, J., & Carter, T. A. (2025) · Physics of Plasmas · doi:10.1063/5.0270755
Exploring parameter dependencies in fusion experiments using interpretable machine learning: A case study on core electron temperature in EAST
Liu, Y., Liao, S., Liu, X., Xu, G., Ding, G., Wang, X., Zang, Q., & Zhou, T. (2025) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2025.115543
Fast end-to-end plasma density profile reconstruction from microwave reflectometer data on EAST
Feng, Y., Wan, C., Huang, J., Li, G., Zhou, Z., Wen, F., Zhang, T., Wang, M. R., Lu, J., Wang, T., et al. (2025) · Nuclear Fusion · doi:10.1088/1741-4326/ae2694
Classifying Alpha Particle Orbit Transitions in Tokamak Fusion Plasmas Using a BiLSTM with Self-attention Mechanism
Xu, J., Gao, B., & Liu, J. (2025) · JUSTC · doi:10.52396/justc-2025-0054
Prediction of the Linear Growth Rate of Tokamak Sawtooth Mode Based on Machine Learning and Physical Models
Zhang, P., Ning, H., Yang, J., Ren, Z., Liu, S., Kuang, J., Wang, Y., & Weihua, W. A. N. G. (2026) · Journal of Fusion Energy · doi:10.1007/s10894-025-00543-x
A Brief History and Evolution of Electron Cyclotron Emission Imaging (ECEI) Systems
Donné, A. J. H., & Zhu, Y. (2025) · International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) · doi:10.1109/irmmw-thz61557.2025.11319801
Comparative Analysis of LSTM and GRU Neural Network Architectures for Plasma Discharge Prediction Using Tokamak Diagnostic Time Series Data
Joshi, R. C. (2025) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn67655.2025.11368018
Machine learning prediction of plasma behavior from discharge configurations on WEST
Wan, C., Almuhisen, F., Moreau, P., Nouailletas, R., Qu, Z., Cho, Y. W., Varennes, R., Lim, K., Li, K., Huang, J., et al. (2026) · Nuclear Fusion · doi:10.1088/1741-4326/ae4917
Noise reduction for self-emission x-ray images in ICF using a physics-informed deep learning framework
Hua, R., Gong, T., Sun, C., Lan, C., Su, Y., Guo, L., & Sun, C. (2026) · Nuclear Fusion · doi:10.1088/1741-4326/ae4b51
Real-time avoidance of the L-mode and H-mode density limit via machine-learned stability metrics
Maris, A., Rea, C., Pau, A., Barr, J. L., Erickson, K. G., Schmitz, L., Yan, Z., Granetz, R., Marmar, E., & the EUROfusion Tokamak Exploitation Team (2026) · Nuclear Fusion · doi:10.1088/1741-4326/ae4efe
Prediction of minor disruptions in Keda Torus eXperiment
Zhang, Y., Wu, Y., Yolbarsop, A., li, H., Yan, W., Rao, X., Xie, J., Lan, T., Liu, A., Mao, W., et al. (2026) · Plasma Science and Technology · doi:10.1088/2058-6272/ae558c
Reconstruction of poloidal magnetic fluxes on EAST based on neural networks with edge electromagnetic diagnostics
Xia, X., Long, F., Liu, J., Zixi, L. I. U., Wu, X., Wan, C., Wu, X., 高, X. 翔. G., Li, G., Luo, Z., et al. (2026) · Plasma Science and Technology · doi:10.1088/2058-6272/ae587a
Developing Autoencoder Models for Multi-Diagnostic Electron Density Processing in Tokamaks within a Scientific Machine Learning Framework
Kaldas, S., Rossi, R., Wyss, I., Gelfusa, M., & Gaudio, P. (2026) · Journal of Fusion Energy · doi:10.1007/s10894-026-00568-w
Uncertainty guided online ensemble for non-stationary data streams in fusion science
Rajput, K., Schram, M., Sammuli, B., & Lin, S. (2026) · Machine Learning with Applications · doi:10.1016/j.mlwa.2026.100894
A procedure for rule extraction from a Self-Organising plasma disruption predictor for JET
Setzu, S., Aymerich, E., Fanni, A., Pisano, F., Sias, G., Cannas, B., contributors, T. J., Maggi, C. F., Abate, D., Abid, N., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-38318-9
Post-alarm time-to-disruption classification in disruptive JET discharges: comparison of SVM, LSTM, and transformer models
Ratta, G. A., Ganzábal, A. G., WPTE Team, & Contributors, J. (2026) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ae6945
Prediction of macroscopic plasma parameters in EAST using machine learning models
Liu, Y., Wu, M., Jia, T., Pan, C., Qian, J. P., He, H., Chen, J., Li, H., Lin, X. L. X., Gao, X., et al. (2026) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ae72cb
Validating a Koopman-quantum hybrid paradigm for diagnostic denoising of fusion devices
Wang, T.-J., Zhang, R.-Q., Qian, L., Song, Y.-T., Lan, T., Liu, H.-Q., & Li, K. (2026) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-026-2962-1
Explainable tokamak-agnostic forecasting of fusion plasma instability via megahertz turbulent fluctuations
Joung, S., Kim, J., Smith, D. R., Gill, K., McKee, G. R., Yan, Z., Geiger, B., Coffee, R., Jalalvand, A., & Kolemen, E. (2026) · Communications Physics · doi:10.1038/s42005-026-02689-2 · Cited by 1
Early prediction of current quench events in the ADITYA tokamak using transformer based data driven model
Agarwal, J., Chaudhury, B., Navadiya, J., Jakhar, S., & Sharma, M. (2026) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2026.115832
Digital twin-driven adaptive monitoring of low-concentration gas ignition and combustion response
Cheng, W., Zheng, X., Fu, C., Xue, S., Yue, S., & Zhang, G. (2026) · Measurement · doi:10.1016/j.measurement.2026.122216
TORQSNet: a physics-inspired spectral neural network for predicting toroidal torque density in tokamak devices
Zhang, H., Liu, Y., Yang, X., & Wang, Z. (2026) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ae8057
From image-based detection to parameter-based prediction of UFO events in KSTAR
Kim, K., Seo, J., & Kim, Y. (2026) · Nuclear Fusion · doi:10.1088/1741-4326/ae91fc
The role of data quality and alignment in cross-tokamak disruption prediction
Shen, C., Zheng, W., Xue, F., Ai, X., Guo, B., Chen, D., Chen, Z., Ding, Y. H., & the J-TEXT team (2026) · Plasma Physics and Controlled Fusion · doi:10.1088/1361-6587/ae9985
Disruption Forecasting and Predictive Analytics With Machine Learning Approaches
Srinivas, T. A. S., Amshavalli, M. M., Biswas, P. C., Gottumukkala, P., Aiswarya, A., Sindhu, V., & Bhoopathy, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6177-2.ch001
Emulating atomic data of Cd-like ions using Gaussian process regression
Dipti, D. (2026) · Physical Review A · doi:10.1103/wfb8-frj9
Data-Driven Prediction of Tritium Transport Parameters for Fusion Reactor Fuel Cycle Modeling
Shu, M., Jiang, W., Cheng, S., Li, Y., Yang, Y., & Chen, Z. (2026) · Journal of Fusion Energy · doi:10.1007/s10894-026-00606-7
FAIRsharing record for: Plasma Metadata Schema
FAIRsharing Team, Becker, M. M., & Becker, M. M. (2022) · FAIRsharing.org · doi:10.25504/fairsharing.5d2dbc
Decision-centered machine learning for fusion plasma-facing components: a prospective on physics-constrained, uncertainty-aware AI design optimization
Juneja, R., Thomas, S. L., Trelewicz, J. R., Staebler, G. M., & Unterberg, E. A. (2026) · MRS Communications · doi:10.1557/s43579-026-01057-z
Precursor warning of NTM/TM trigger regime in EAST based on supervised learning with rolling-aware hard-negative training
Long, F., Zhang, Y., Zhang, Y., zhao, Y., Liu, A., Zhou, C., Liu, Z., Mao, W., Xie, J., Zhang, J., et al. (2026) · Frontiers in Physics · doi:10.3389/fphy.2026.1924532

Authors: Kates-Harbeck, J., Svyatkovskiy, A., & Tang, W.
Affiliations: Microsoft (United States) (US)
Princeton University (US)
ORCID: Tang, William
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2019-04
Volume: 568
Issue: 7753
Pages: 526-531
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/s41586-019-1116-4
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

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

Predicting disruptive instabilities in controlled fusion plasmas through deep learning

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks