Magnetic control of tokamak plasmas through deep reinforcement learning
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DOI: 10.1038/s41586-021-04301-9
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Cited by:
- Kai Zhao & Jia Song & Yunlong Hu & Xiaowei Xu & Yang Liu, 2022. "Deep Deterministic Policy Gradient-Based Active Disturbance Rejection Controller for Quad-Rotor UAVs," Mathematics, MDPI, vol. 10(15), pages 1-15, July.
- Caputo, Cesare & Cardin, Michel-Alexandre & Ge, Pudong & Teng, Fei & Korre, Anna & Antonio del Rio Chanona, Ehecatl, 2023. "Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning," Applied Energy, Elsevier, vol. 335(C).
- Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
- Fuhao Ji & Auralee Edelen & Ryan Roussel & Xiaozhe Shen & Sara Miskovich & Stephen Weathersby & Duan Luo & Mianzhen Mo & Patrick Kramer & Christopher Mayes & Mohamed A. K. Othman & Emilio Nanni & Xiji, 2024. "Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction," Nature Communications, Nature, vol. 15(1), pages 1-7, 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.
- Zhang, Tianhao & Dong, Zhe & Huang, Xiaojin, 2024. "Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning," Energy, Elsevier, vol. 286(C).
- Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
- Yang, Kaiyuan & Huang, Houjing & Vandans, Olafs & Murali, Adithya & Tian, Fujia & Yap, Roland H.C. & Dai, Liang, 2023. "Applying deep reinforcement learning to the HP model for protein structure prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
- Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
- Maryam Ghalkhani & Saeid Habibi, 2022. "Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application," Energies, MDPI, vol. 16(1), pages 1-16, December.
- Jiyu Cui & Fang Wu & Wen Zhang & Lifeng Yang & Jianbo Hu & Yin Fang & Peng Ye & Qiang Zhang & Xian Suo & Yiming Mo & Xili Cui & Huajun Chen & Huabin Xing, 2023. "Direct prediction of gas adsorption via spatial atom interaction learning," Nature Communications, Nature, vol. 14(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.
- Stefano Bianchini & Moritz Muller & Pierre Pelletier, 2023. "Drivers and Barriers of AI Adoption and Use in Scientific Research," Papers 2312.09843, arXiv.org, revised Feb 2024.
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