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Garland Group

Griffith University

Through a range of theoretical and computational methods we have a great interest in modeling electron transport and plasmas, from low-temperature to MCF plasmas. Examples of model techniques include fluid/conservation equation models for electron and plasma transport, collisional-radiative modeling, and kinetic equation modeling. Particular focus is paid to trying to employ the appropriate atomic physics data in simulations and models. Recently, physically-based deep learning and uncertainty quantification methods have been explored.


Science 2 (N34) Room 0.23; School of Environment and Science - Applied Mathematics and Physics; Griffith University Nathan Campus; QLD 4111; AUSTRALIA
Group Website
Phone: +617 3735 3880

  • [1] N. A. Garland et al., "Progress towards high fidelity collisional-radiative model surrogates for rapid in-situ evaluation", Machine Learning and the Physical Sciences Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS) (2020). [link to article]
  • [2] R. Maulik et al., "Neural network representability of fully ionized plasma fluid model closures", Physics of Plasmas 27, 072106 (2020). [link to article]
  • [3] N. A. Garland et al., "Impact of a minority relativistic electron tail interacting with a thermal plasma containing high-atomic-number impurities", Physics of Plasmas 27, 040702 (2020). [link to article]
  • [4] I. Simonović et al., "Electron transport and negative streamers in liquid xenon", Plasma Sources Science and Technology 28, 015006 (2019). [link to article]
  • [5] N. A Garland et al., "Electron swarm and streamer transport across the gas–liquid interface: a comparative fluid model study", Plasma Sources Science and Technology 27, 105004 (2018). [link to article]
  • [6] R. D White et al., "Electron transport in biomolecular gaseous and liquid systems: theory, experiment and self-consistent cross-sections", Plasma Sources Science and Technology 27, 053001 (2018). [link to article]
  • [7] N. A Garland et al., "Approximating the nonlinear density dependence of electron transport coefficients and scattering rates across the gas–liquid interface", Plasma Sources Science and Technology 27, 024002 (2018). [link to article]
  • [8] N. A Garland et al., "Unified fluid model analysis and benchmark study for electron transport in gas and liquid analogs", Plasma Sources Science and Technology 26, 075003 (2017). [link to article]
  • [9] R. D. White et al., "Electron swarm transport in THF and water mixtures", The European Physical Journal D 68, 125 (2014). [link to article]
  • [10] N. A. Garland et al., "Transport properties of electron swarms in tetrahydrofuran under the influence of an applied electric field", Physical Review A 88, 062712 (2013). [link to article]


Collisional-Radiative Processes Machine Learning Plasma Fluid Models Uncertainty Quantification