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This content will become publicly available on January 16, 2021

Title: Analysis of NIF scaling using physics informed machine learning

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]
  1. The Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11790, USA
  2. Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1591980
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Physics of Plasmas
Additional Journal Information:
Journal Name: Physics of Plasmas Journal Volume: 27 Journal Issue: 1; Journal ID: ISSN 1070-664X
Publisher:
American Institute of Physics
Country of Publication:
United States
Language:
English

Citation Formats

Hsu, Abigail, Cheng, Baolian, and Bradley, Paul A. Analysis of NIF scaling using physics informed machine learning. United States: N. p., 2020. Web. doi:10.1063/1.5130585.
Hsu, Abigail, Cheng, Baolian, & Bradley, Paul A. Analysis of NIF scaling using physics informed machine learning. United States. doi:10.1063/1.5130585.
Hsu, Abigail, Cheng, Baolian, and Bradley, Paul A. Wed . "Analysis of NIF scaling using physics informed machine learning". United States. doi:10.1063/1.5130585.
@article{osti_1591980,
title = {Analysis of NIF scaling using physics informed machine learning},
author = {Hsu, Abigail and Cheng, Baolian and Bradley, Paul A.},
abstractNote = {},
doi = {10.1063/1.5130585},
journal = {Physics of Plasmas},
number = 1,
volume = 27,
place = {United States},
year = {2020},
month = {1}
}

Journal Article:
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This content will become publicly available on January 16, 2021
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