activity
20142023
most citedDetermining cross sections from transport coefficients using deep neural networks

34 citations · 90 across the 10 of their papers we have counts for

collaborators

10 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…

cond-mat.stat-mech2021

Analysis of a generalised Boltzmann equation for anomalous diffusion under time-dependent fields

Alex D. C. Myhill, Peter W. Stokes, Bronson Philippa +1

The generalised Boltzmann equation which treats the combined localised and delocalised nature of transport present in certain materials is extended to accommodate time-dependent fi…

physics.chem-ph2021★ 8 cited

Toward a complete and comprehensive cross section database for electron scattering from NO using machine learning

Peter W. Stokes, Ronald D. White, Laurence Campbell +1

We review experimental and theoretical cross sections for electron scattering in nitric oxide (NO) and form a comprehensive set of plausible cross sections. To assess the accuracy…

physics.plasm-ph2021★ 9 cited

An improved set of electron-THFA cross sections refined through a neural network-based analysis of swarm data

Peter W. Stokes, Sean P. Foster, Madalyn J. E. Casey +6

We review experimental and theoretical cross sections for electron transport in -tetrahydrofurfuryl alcohol (THFA) and, in doing so, propose a plausible complete set. To assess…

physics.plasm-ph2020★ 18 cited

Self-consistent electron-THF cross sections derived using data-driven swarm analysis with a neural network model

Peter W. Stokes, Madalyn J. E. Casey, Daniel G. Cocks +4

We present a set of self-consistent cross sections for electron transport in gaseous tetrahydrofuran (THF), that refines the set published in our previous study [J. de Urquijo et a…

physics.plasm-ph2019★ 34 cited

Determining cross sections from transport coefficients using deep neural networks

Peter W. Stokes, Daniel G. Cocks, Michael J. Brunger +1

We present a neural network for the solution of the inverse swarm problem of deriving cross sections from swarm transport data. To account for the uncertainty inherent to this some…