3 papers
physics.comp-ph2023
An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients
Dale L Muccignat, Gregory G Boyle, Nathan A Garland +2
We propose improvements to the Artificial Neural Network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent…
physics.comp-ph2020
Neural network representability of fully ionized plasma fluid model closures
Romit Maulik, Nathan A. Garland, Xian-Zhu Tang +1
The closure problem in fluid modeling is a well-known challenge to modelers aiming to accurately describe their system of interest. Over many years, analytic formulations in a wide…
physics.plasm-ph2019
Impact of a minority relativistic electron tail interacting with a thermal plasma containing high-atomic-number impurities
Nathan A. Garland, Hyun-Kyung Chung, Christopher J. Fontes +6
A minority relativistic electron component can arise in both laboratory and naturally-occurring plasmas. In the presence of high-atomic-number ion species, the ion charge state dis…