1 citations · 1 across the 10 of their papers we have counts for
3 papers · 2 filters
Hierarchical Framework of Runaway Electrons using Deep Learning
Tyler Mark, Christopher McDevitt
We present an adjoint deep learning framework describing the evolution of fluid moments and the energy distribution of the runaway electron (RE) population. We demonstrate that a c…
A Physics-Informed Neural Network for Solving the Quasi-static Magnetohydrodynamic Equations
Jonathan S. Arnaud, Christopher J. McDevitt, Golo Wimmer +1
A physics-informed neural network (PINN) is developed, for the first time, to learn the time-dependent quasi-static magnetohydrodynamic (MHD) equations in axisymmetric tokamak geom…
A Deep Learning Approach to Describing the Plasma Sheath
Ethan Webb, Yuzhi Li, Christopher McDevitt
Despite their ubiquity, the rich physics present in a plasma sheath has inhibited the development of a generally applicable description of this critical region. The present study u…