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Title: Machine learning for analysis of atomic spectral data

Abstract

Physics based forward models are the basis on which many experimental diagnostics are interpreted. For some diagnostics, models can be computationally expensive which precludes their use in real time analysis. Reduced models have the potential to capture sufficient physics thereby enabling the desired real time analysis. Using statistical inference and machine learning techniques the application of reduced models for inversion of atomic spectral data used to diagnose magnetic fields in a plasma will be examined. Two approaches are considered, (a) a reduction of the forward model where traditional inversion can be performed on the proxy model, and (b) a reduction of the direct inverse where parameters are a function of measured signal. The resulting inversion is sufficiently fast to be utilized in an online context for digital twinning, and ultimately real-time prediction, design, and control of plasma systems, such as tokamaks. Furthermore, these methods will be demonstrated on both simulated and experimentally measured data.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Manchester (United Kingdom)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1570912
Alternate Identifier(s):
OSTI ID: 1575801
Grant/Contract Number:  
AC05-00OR22725; FG02-04ER54761
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Quantitative Spectroscopy and Radiative Transfer
Additional Journal Information:
Journal Volume: 240; Journal Issue: C; Journal ID: ISSN 0022-4073
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS

Citation Formats

Cianciosa, Mark, Law, Kody J. H., Martin, Elijah H., and Green, David L. Machine learning for analysis of atomic spectral data. United States: N. p., 2019. Web. doi:10.1016/j.jqsrt.2019.106671.
Cianciosa, Mark, Law, Kody J. H., Martin, Elijah H., & Green, David L. Machine learning for analysis of atomic spectral data. United States. doi:10.1016/j.jqsrt.2019.106671.
Cianciosa, Mark, Law, Kody J. H., Martin, Elijah H., and Green, David L. Fri . "Machine learning for analysis of atomic spectral data". United States. doi:10.1016/j.jqsrt.2019.106671.
@article{osti_1570912,
title = {Machine learning for analysis of atomic spectral data},
author = {Cianciosa, Mark and Law, Kody J. H. and Martin, Elijah H. and Green, David L.},
abstractNote = {Physics based forward models are the basis on which many experimental diagnostics are interpreted. For some diagnostics, models can be computationally expensive which precludes their use in real time analysis. Reduced models have the potential to capture sufficient physics thereby enabling the desired real time analysis. Using statistical inference and machine learning techniques the application of reduced models for inversion of atomic spectral data used to diagnose magnetic fields in a plasma will be examined. Two approaches are considered, (a) a reduction of the forward model where traditional inversion can be performed on the proxy model, and (b) a reduction of the direct inverse where parameters are a function of measured signal. The resulting inversion is sufficiently fast to be utilized in an online context for digital twinning, and ultimately real-time prediction, design, and control of plasma systems, such as tokamaks. Furthermore, these methods will be demonstrated on both simulated and experimentally measured data.},
doi = {10.1016/j.jqsrt.2019.106671},
journal = {Journal of Quantitative Spectroscopy and Radiative Transfer},
number = C,
volume = 240,
place = {United States},
year = {2019},
month = {10}
}

Journal Article:
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This content will become publicly available on October 4, 2020
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