Magnetic control of tokamak plasmas through deep reinforcement learning
Author
Abstract
Suggested Citation
DOI: 10.1038/s41586-021-04301-9
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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).
- 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.
- 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).
- 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.
- 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.
- 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.
- Malte Reinschmidt & József Fortágh & Andreas Günther & Valentin V. Volchkov, 2024. "Reinforcement learning in cold atom experiments," Nature Communications, Nature, vol. 15(1), pages 1-11, 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.
- 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.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:602:y:2022:i:7897:d:10.1038_s41586-021-04301-9. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.