44 citations · 86 across the 3 of their papers we have counts for
7 papers
Enabling particle applications for exascale computing platforms
Susan M Mniszewski, James Belak, Jean-Luc Fattebert +21
The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. Within ECP, the Co-design Center for Particle Applic…
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…
Geometric Electrostatic Particle-In-Cell Algorithm on Unstructured Meshes
Zhenyu Wang, Hong Qin, Benjamin Sturdevant +1
We present a geometric Particle-in-Cell (PIC) algorithm on two-dimensional (2D) unstructured meshes for studying electrostatic perturbations in magnetized plasmas. In this method,…
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.…
A tight-coupling scheme sharing minimum information across a spatial interface between gyrokinetic turbulence codes
Julien Dominski, Seung-Hoe Ku, Choong-Seock Chang +5
A new scheme that tightly couples kinetic turbulence codes across a spatial interface is introduced. This scheme evolves from considerations of competing strategies and down-select…