1 citations · 1 across the 10 of their papers we have counts for
10 papers
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…
An Adjoint Formulation of Energetic Particle Confinement
Christopher J. McDevitt, Jonathan S. Arnaud
An adjoint formulation of energetic particle confinement in axisymmetric tokamak geometry is derived and evaluated using a physics-informed neural network (PINN). The PINN estimate…
A Runaway Electron Avalanche Surrogate for Partially Ionized Plasmas
Jonathan S. Arnaud, Xian-Zhu Tang, Christopher J. McDevitt
A physics-constrained deep learning surrogate that predicts the exponential ``avalanche'' growth rate of runaway electrons (REs) for a plasma containing partially ionized impuritie…
An Efficient Surrogate Model of Secondary Electron Formation and Evolution
Christopher J. McDevitt, Jonathan Arnaud, Xian-Zhu Tang
This work extends the adjoint-deep learning framework for runaway electron (RE) evolution developed in Ref. [C. McDevitt et al., A physics-constrained deep learning treatment of ru…
A Physics-Constrained Deep Learning Treatment of Runaway Electron Dynamics
Christopher J. McDevitt, Jonathan Arnaud, Xian-Zhu Tang
An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A dist…
A physics-constrained deep learning surrogate model of the runaway electron avalanche growth rate
Jonathan S. Arnaud, Tyler Mark, Christopher J. McDevitt
A surrogate model of the runaway electron avalanche growth rate in a magnetic fusion plasma is developed. This is accomplished by employing a physics-informed neural network (PINN)…
A reduced kinetic method for investigating non-local ion heat transport in ideal multi-species plasmas
Nicholas Mitchell, David Chapman, Christopher McDevitt +2
A reduced kinetic method (RKM) with a first-principle collision operator is introduced in a 1D2V planar geometry and implemented in a computationally inexpensive code to investigat…
The Impact of Collisionality on the Runaway Electron Avalanche during a Tokamak Disruption
Jonathan Arnaud, Christopher McDevitt
The exponential growth (avalanching) of runaway electrons (REs) during a tokamak disruption continues to be a large uncertainty in RE modeling. The present work investigates the im…