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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 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…