97 citations · 343 across the 11 of their papers we have counts for
11 papers
2022 Review of Data-Driven Plasma Science
Rushil Anirudh, Rick Archibald, M. Salman Asif +60
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data…
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…
Near real-time streaming analysis of big fusion data
Ralph Kube, R. Michael Churchill, CS Chang +6
While experiments on fusion plasmas produce high-dimensional data time series with ever increasing magnitude and velocity, data analysis has been lagging behind this development. F…
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…
Analysis of equilibrium and turbulent fluxes across the separatrix in a gyrokinetic simulation
I. Keramidas Charidakos, J. R. Myra, S. Parker +4
The SOL width is a parameter of paramount importance in modern tokamaks as it controls the power density deposited at the divertor plates, critical for plasma-facing material survi…
Electrostatic gyrokinetic simulation of global tokamak boundary plasma and the generation of nonlinear intermittent turbulence
S. Ku, R. M. Churchill, C. S. Chang +5
Boundary plasma physics plays an important role in tokamak confinement, but is difficult to simulate in a gyrokinetic code due to the scale-inseparable nonlocal multi-physics in ma…
A fast low-to-high mode bifurcation dynamics in a tokamak edge plasma gyrokinetic simulation
C. S. Chang, S. Ku, G. R. Tynan +6
Transport barrier formation and its relation to sheared flows in fluids and plasmas are of fundamental interest in various natural and laboratory observations and of critical impor…