9 citations · 13 across the 3 of their papers we have counts for
3 papers
physics.comp-ph2024★ 3 cited
AI-Machine Learning-Enabled Tokamak Digital Twin
William Tang, Eliot Feibush, Ge Dong +9
In addressing the Department of Energy's April, 2022 announcement of a Bold Decadal Vision for delivering a Fusion Pilot Plant by 2035, associated software tools need to be develop…
physics.plasm-ph2022★ 9 cited
Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System
William Tang, Ge Dong, Jayson Barr +17
This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 N…
physics.plasm-ph2020★ 1 cited
Topology optimization of permanent magnets for stellarators
Caoxiang Zhu, Kenneth Hammond, Thomas Brown +5
We introduce a topology optimization method to design permanent magnets for advanced stellarators. Recent researches show that permanent magnets have great potentials to simplify s…