10 papers
SafeDivertor: Faithful Divertor Heat Flux Reconstruction from Macroscopic Plasma State Signals via Time-Frequency Prior Exploitation
Hao Si, Zehua Chen, Qingquan Yang +8
Divertor heat-flux analysis is essential for understanding plasma-wall interactions and protecting plasma-facing components in magnetic-confinement fusion devices, while convention…
Physics-Informed Neural Networks for Complex Eigenfrequency Identification and Mode Structure Reconstruction of the Ground-State ITG Branch
Dengdi Sun, Bingbing Zhang, Xiao Wang +5
Physics-informed neural networks (PINNs) combine sparse observations with physical equations, providing an important approach for modeling complex plasma processes and inferring un…
Visible-Light Imaging Diagnosis of Neutral Particle Emission Tomography in the Tokamak Divertor: An Efficient Transformer-based Surrogate Model
Xiao Wang, Hao Si, Qiang Chen +8
Nuclear fusion has made significant progress in recent years and is expected to become one of the most important pathways to addressing global energy challenges. This paper focuses…
Hierarchical Multi-to-Single-Modal Knowledge Distillation for Disruption Prediction in EAST
Qiang Chen, Xiao Wang, Hao Si +9
Plasma disruption is a critical threat to tokamak safety. Existing data-driven predictors mainly rely on time-series diagnostic signals, while visible images provide complementary…
Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer
Zikang Yan, Xiao Wang, Qingquan Yang +6
Accurate modeling of the divertor temperature field is essential for preventing material melting and damage and for extending the service life of fusion devices. However, conventio…
HGTS-Former: Hierarchical HyperGraph Transformer for Multivariate Time Series Analysis
Hao Si, Xiao Wang, Fan Zhang +5
Multivariate time series analysis has long been one of the key research topics in the field of artificial intelligence. However, analyzing complex time series data remains a challe…