104 citations · 372 across the 16 of their papers we have counts for
4 papers · 1 filter
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…
FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural Networks
Zhe Bai, Xishuo Wei, William Tang +3
Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel model reduction methods,…
Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science
Ge Dong, Kyle Gerard Felker, Alexey Svyatkovskiy +2
We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma scien…
Initializing and stabilizing variational multistep algorithms for modeling dynamical systems
C. L. Ellison, J. W. Burby, J. M. Finn +2
Backward error initialization and parasitic mode control are well-suited for use in algorithms that arise from a discrete variational principle on phase-space dynamics. Dynamical s…