44 citations · 61 across the 4 of their papers we have counts for
8 papers
Machine learning accelerated particle-in-cell plasma simulations
R. Kube, R. M. Churchill, B. Sturdevant
Particle-In-Cell (PIC) methods are frequently used for kinetic, high-fidelity simulations of plasmas. Implicit formulations of PIC algorithms feature strong conservation properties…
Constructing a new predictive scaling formula for ITER's divertor heat-load width informed by a simulation-anchored machine learning
C. S. Chang, S. Ku, R. Hager +6
Understanding and predicting divertor heat-load width is a critically important problem for an easier and more robust operation of ITER with high fusion gain. Previous predic…
Training neural networks under physical constraints using a stochastic augmented Lagrangian approach
Alp Dener, Marco Andres Miller, Randy Michael Churchill +2
We investigate the physics-constrained training of an encoder-decoder neural network for approximating the Fokker-Planck-Landau collision operator in the 5-dimensional kinetic fusi…
Encoder-decoder neural network for solving the nonlinear Fokker-Planck-Landau collision operator in XGC
M. A. Miller, R. M. Churchill, A. Dener +3
An encoder-decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker-Planck-Landau collision operator.…
Comparison of edge turbulence characteristics between DIII-D and C-Mod simulations with XGC1
I. Keramidas Charidakos, J. R. Myra, S. Ku +4
The physical processes taking place at the edge region are crucial for the operation of tokamaks as they govern the interaction of hot plasma with the vessel walls. Numerical model…
Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices
R. M. Churchill, the DIII-D team
The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizi…