1 citations · 1 across the 7 of their papers we have counts for
4 papers · 2 filters
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)…
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…