30 citations · 37 across the 4 of their papers we have counts for
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…
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…
A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks
Chenguang Wan, Zhi Yu, Alessandro Pau +2
Nuclear fusion represents one of the best alternatives for a sustainable source of clean energy. Tokamaks allow to confine fusion plasma with magnetic fields and one of the main ch…
A data management system for machine learning research of tokamak
Chenguang Wan, Zhi Yu, Xiaojuan Liu +3
In recent years, machine learning (ML) research methods have received increasing attention in the tokamak community. The conventional database (i.e., MDSplus for tokamak) of experi…