Machine-Learning-Assisted Analysis of Visible Spectroscopy in Pulsed-Power-Driven Plasmas
We use machine-learning (ML) models to predict ion density and electron temperature from visible emission spectra, in a high-energy density pulsed-power-driven aluminum plasma, generated by an exploding wire array. Radiation transport simulations, which use spectral emissivity and opacity values gen...
| Main Authors: | , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2024
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| Online Access: | https://hdl.handle.net/1721.1/155296 |