4 papers
Plasma GraphRAG: Physics-Grounded Parameter Selection for Gyrokinetic Simulations
Ruichen Zhang, Feda AlMuhisen, Chenguang Wan +6
Accurate parameter selection is fundamental to gyrokinetic plasma simulations, yet current practices rely heavily on manual literature reviews, leading to inefficiencies and incons…
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…
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…