5 papers
Linear-Stability-Guided Two-Stage Surrogate Model for Nonlinear ITG-TEM Turbulence
Y. W. Cho, C. G. Wan, Z. S. Qu +7
We develop a two-stage surrogate model framework to predict the linear and nonlinear properties of ITG-TEM turbulence from GENE gyrokinetic simulations. The first stage uses a hier…
Optimal-Transport-Guided Functional Flow Matching for Turbulent Field Generation in Hilbert Space
Li Kunpeng, Wan Chenguang, Qu Zhisong +5
High-fidelity modeling of turbulent flows requires capturing complex spatiotemporal dynamics and multi-scale intermittency, posing a fundamental challenge for traditional knowledge…
Machine learning prediction of plasma behavior from discharge configurations on WEST
Chenguang Wan, Feda Almuhisen, Philippe Moreau +10
Accurately predicting plasma behavior based on discharge configurations is essential for the safe and efficient operation of tokamak experiments. While physics-based integrated mod…
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…
Reconstruction of Poloidal Magnetic Fluxes on EAST based on Neural Networks with Measured Signals
Feifei Long, Xiangze Xia, Jian Liu +9
The accurate construction of tokamak equilibria, which is critical for the effective control and optimization of plasma configurations, depends on the precise distribution of magne…