28 citations · 47 across the 8 of their papers we have counts for
6 papers · 1 filter
Adaptive Anomaly Detection Disruption Prediction Starting from First Discharge on Tokamak
Xinkun Ai
Plasma disruption presents a significant challenge in tokamak fusion, where it can cause severe damage and economic losses. Current disruption predictors mainly rely on data-driven…
Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder
Xinkun Ai, Wei Zheng, Ming Zhang +12
In the initial stages of operation for future tokamak, facing limited data availability, deploying data-driven disruption predictors requires optimal performance with minimal use o…
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…
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection
Xinkun Ai, Wei Zheng, Ming Zhang +12
The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-dr…
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…
Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks
J. Zhu, C. Rea, R. S. Granetz +10
Next generation high performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operat…