Predicting disruptive instabilities in controlled fusion plasmas through deep learning
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DOI: 10.1038/s41586-019-1116-4
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Cited by:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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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