25 citations · 27 across the 4 of their papers we have counts for
4 papers
Experiment-free disruption prediction for new devices enabled by synthetic diagnostic data augmentation
Zhiqiang Liu, Fengming Xue, Shiwei Xue +4
Deep learning based approaches have shown great promise in cross-device disruption prediction for tokamaks, however, the robustness of these models heavily relies on massive amount…
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…
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…
IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction
Chengshuo Shen, Wei Zheng, Yonghua Ding +10
Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. Understanding why a predictor makes a certain prediction can be as…