11 papers
FusionMMT: A Unified Multimodal and Multitask Learning Framework for Nuclear Fusion
Qiang Chen, Xiao Wang, Qingquan Yang +5
With the growing global demand for energy, nuclear fusion has emerged as a promising direction for future clean energy. Tokamaks represent one of the leading approaches to magnetic…
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…
Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Network
Xiao Wang, Zikang Yan, Hao Si +5
Estimating heat flux in the nuclear fusion device EAST is a critically important task. Traditional scientific computing methods typically model this process using the Finite Elemen…
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…
XiHeFusion: Harnessing Large Language Models for Science Communication in Nuclear Fusion
Xiao Wang, Qingquan Yang, Fuling Wang +12
Nuclear fusion is one of the most promising ways for humans to obtain infinite energy. Currently, with the rapid development of artificial intelligence, the mission of nuclear fusi…
Exploiting Memory-aware Q-distribution Prediction for Nuclear Fusion via Modern Hopfield Network
Qingchuan Ma, Shiao Wang, Tong Zheng +4
This study addresses the critical challenge of predicting the Q-distribution in long-term stable nuclear fusion task, a key component for advancing clean energy solutions. We intro…
Multi-modal Fusion based Q-distribution Prediction for Controlled Nuclear Fusion
Shiao Wang, Yifeng Wang, Qingchuan Ma +5
Q-distribution prediction is a crucial research direction in controlled nuclear fusion, with deep learning emerging as a key approach to solving prediction challenges. In this pape…