Deep sequence to sequence learning-based prediction of major disruptions in ADITYA tokamak
Author:
Funder
Board of Research in Nuclear Sciences
Publisher
IOP Publishing
Subject
Condensed Matter Physics,Nuclear Energy and Engineering
Link
https://iopscience.iop.org/article/10.1088/1361-6587/ac234c/pdf
Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Overview of physics results from the ADITYA-U tokamak and future experiments;Nuclear Fusion;2024-08-23
2. Parsimonious statistical techniques for the detection of drifts toward dangerous operational conditions in tokamaks;Plasma Physics and Controlled Fusion;2024-08-01
3. Estimation of Plasma Vertical Position by Long-Short Term Memory Network with Time2Vec in a Small Tokamak Device PHiX;Plasma and Fusion Research;2024-07-16
4. Automated labelling and correlation analysis of diagnostic signals from ADITYA tokamak for developing AI-based disruption mitigation systems;Radiation Effects and Defects in Solids;2024-07-03
5. MHD spectrogram contribution to disruption prediction using Convolutional Neural Networks;Fusion Engineering and Design;2024-07
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