Deep-Spec: Can we predict the temperature and density state of the COAX target from absorption spectra information with a deep learning network on simulated data?
Network structure for a generative surrogate model Dataset for generative surrogate model Various resultsCan we predict the temperature and density state of the COAX target from absorption spectra information with a deep learning network on simulated data?10/16/24
Authors
- Chris L. Fryer (ORCID: https://orcid.org/0000-0003-2624-0056)
- Todd J. Urbatsch (ORCID: https://orcid.org/0000-0002-7257-608X)
- Wyatt Madden (ORCID: https://orcid.org/0000-0002-9792-7077)
- Kelsey Adler
- Shane Coffing (ORCID: https://orcid.org/0000-0002-7083-3038)
- John Tipton (ORCID: https://orcid.org/0000-0002-6135-8191)
- Jacob Feltman
Institutions
- Los Alamos National Laboratory (US)
Publication Details
- Published
- 2024-10-17
- DOI
- https://doi.org/10.2172/2472568
- Primary Topic
- Mass Spectrometry Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- National Nuclear Security Administration
- Los Alamos National Laboratory