activity
20192026
most citedNeural network representability of fully ionized plasma fluid model closures

26 citations · 39 across the 7 of their papers we have counts for

collaborators

7 papers

physics.plasm-ph2026

Collisional-radiative data for tokamak disruption mitigation modeling

Prashant Sharma, Christopher J. Fontes, Dmitry V. Fursa +6

Effective tokamak disruption mitigation is crucial for ensuring the safety and integrity of fusion power reactors. Accurate collisional-radiative (CR) modeling of a radiative plasm…

physics.plasm-ph2026

Hybrid collisional-radiative modeling for high-fidelity atomic kinetics

Prashant Sharma, Christopher J. Fontes, Mark Zammit +3

The fidelity of collisional-radiative (CR) models is critical for advancing our understanding of radiative properties and ionization balance in fusion plasmas. In this work, we pre…

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.chem-ph2023

Semi-empirical analysis of leptons in gases in crossed electric and magnetic fields, Part II: Transverse compression of muon beams

Malte Hildebrandt, Robert E Robson, Nathan A Garland

This article employs fluid equations to analyse muon beams in gases subject to crossed electric and magnetic fields, focussing in particular on a scheme proposed by D. Taqqu in 200…

physics.plasm-ph2021

Efficient training of artificial neural network surrogates for a collisional-radiative model through adaptive parameter space sampling

Nathan A. Garland, Romit Maulik, Qi Tang +2

Reliable plasma transport modeling for magnetic confinement fusion depends on accurately resolving the ion charge state distribution and radiative power losses of the plasma. These…

physics.comp-ph2020★ 26 cited

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★ 13 cited

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…