5 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…
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…
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…