2 citations · 2 across the 3 of their papers we have counts for
3 papers
physics.plasm-ph2024
Attention-aware convolutional neural networks for identification of magnetic islands in the tearing mode on EAST tokamak
Feifei Long, Yian Zhao, Yunjiao Zhang +16
The tearing mode, a large-scale MHD instability in tokamak, typically disrupts the equilibrium magnetic surfaces, leads to the formation of magnetic islands, and reduces core elect…
physics.plasm-ph2023
Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation
Chengshuo Shen, Wei Zheng, Bihao Guo +11
The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in dis…
physics.plasm-ph2022★ 2 cited
Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor
Wei Zheng, Fengming Xue, Ming Zhang +12
Predicting disruptions across different tokamaks is a great obstacle to overcome. Future tokamaks can hardly tolerate disruptions at high performance discharge. Few disruption disc…