4 papers
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…
Reconstructing High-fidelity Plasma Turbulence with Data-driven Tuning of Diffusion in Low Resolution Grids
Kunpeng Li, Youngwoo Cho, Xavier Garbet +6
Developing physically consistent closure models is a longstanding challenge in simulating plasma turbulence, even in minimal systems such as the two-field Hasegawa-Wakatani (HW) mo…
Extracting a stochastic model for predator-prey dynamic of turbulence and zonal flows with limited data
J. C. Huang, Z. S. Qu, R. Varennes +6
Understanding the interaction between turbulence and zonal flows is critical for modeling turbulence transport in fusion plasmas, often described through predator-prey dynamics. Ho…
A high-fidelity surrogate model for the ion temperature gradient (ITG) instability using a small expensive simulation dataset
Chenguang Wan, Youngwoo Cho, Zhisong Qu +7
One of the main challenges in building high-fidelity surrogate models of tokamak turbulence is the substantial demand for high-quality data. Typically, producing high-quality data…