13 citations · 25 across the 9 of their papers we have counts for
3 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-Informed Deep Learning Description of Knudsen Layer Reactivity Reduction
Christopher J. McDevitt, Xian-Zhu Tang
A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and outpu…