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Predicting disruptive instabilities in controlled fusion plasmas through deep learning

Author

Listed:
  • Julian Kates-Harbeck

    (Harvard University
    Harvard University
    Princeton Plasma Physics Laboratory)

  • Alexey Svyatkovskiy

    (Princeton University
    Microsoft, One Microsoft Way)

  • William Tang

    (Princeton Plasma Physics Laboratory
    Princeton University)

Abstract

Nuclear fusion power delivered by magnetic-confinement tokamak reactors holds the promise of sustainable and clean energy1. The avoidance of large-scale plasma instabilities called disruptions within these reactors2,3 is one of the most pressing challenges4,5, 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) project6 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-based5 and classical machine-learning7–11 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-D12) and the world (Joint European Torus, JET13), 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.

Suggested Citation

  • Julian Kates-Harbeck & Alexey Svyatkovskiy & William Tang, 2019. "Predicting disruptive instabilities in controlled fusion plasmas through deep learning," Nature, Nature, vol. 568(7753), pages 526-531, April.
  • Handle: RePEc:nat:nature:v:568:y:2019:i:7753:d:10.1038_s41586-019-1116-4
    DOI: 10.1038/s41586-019-1116-4
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    Citations

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    Cited by:

    1. SeongMoo Yang & Jong-Kyu Park & YoungMu Jeon & Nikolas C. Logan & Jaehyun Lee & Qiming Hu & JongHa Lee & SangKyeun Kim & Jaewook Kim & Hyungho Lee & Yong-Su Na & Taik Soo Hahm & Gyungjin Choi & Joseph, 2024. "Tailoring tokamak error fields to control plasma instabilities and transport," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. S. K. Kim & R. Shousha & S. M. Yang & Q. Hu & S. H. Hahn & A. Jalalvand & J.-K. Park & N. C. Logan & A. O. Nelson & Y.-S. Na & R. Nazikian & R. Wilcox & R. Hong & T. Rhodes & C. Paz-Soldan & Y. M. Jeo, 2024. "Highest fusion performance without harmful edge energy bursts in tokamak," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Andrea Murari & Riccardo Rossi & Teddy Craciunescu & Jesús Vega & Michela Gelfusa, 2024. "A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    4. Andrey Gorshenin & Victor Kuzmin, 2022. "Statistical Feature Construction for Forecasting Accuracy Increase and Its Applications in Neural Network Based Analysis," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
    5. Tobias Thomas & Dominik Straub & Fabian Tatai & Megan Shene & Tümer Tosik & Kristian Kersting & Constantin A. Rothkopf, 2024. "Modelling dataset bias in machine-learned theories of economic decision-making," Nature Human Behaviour, Nature, vol. 8(4), pages 679-691, April.
    6. Uday K. Chakraborty, 2019. "Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm," Energies, MDPI, vol. 12(16), pages 1-26, August.

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