Automatic Level-Grouping of Complexity-Reduced, Radiative Atomic Kinetics.

High-fidelity plasma simulations involving atomic kinetics requires state-space completeness often accompanied by many types of atomic transitions. The computational expense associated with this requirement drove the development of a collisional-radiative (CR) modeling package simulating reduced-order atomic kinetics through an automatic level-grouping process. Starting from the novel Boltzmann grouping methodology devised by Le et al.\footnote{Le et al. \textit{Phys Plasmas} 20, 1-19 (2013)}, spectral clustering techniques taken from machine learning automated the construction of appropriate level groups for time-dependent simulations. While the results from these simulations captured global plasma evolution well for various plasma conditions\footnote{Abrantes et al. \textit{J Comput Phys} 407, (2020)}, obtaining accurate radiative spectra remained problematic throughout the investigation. Further refinement of the clustering methodology to better account for spectral accuracy is therefore needed. In this work, a preliminary investigation adapting the clustering method to better capture these radiative effects will be introduced to facilitate the rapid development of automatically-reduced CR models that better approximate radiative effects for a wider range of plasma regimes.