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Title: Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws.

Abstract

Abstract not provided.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
ASC Advanced Machine Learning
OSTI Identifier:
1569346
Report Number(s):
SAND2019-11191R
679800
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Lee, Kookjin, Lee, Kookjin, Lee, Kookjin, Lee, Kookjin, Carlberg, Kevin, and Carlberg, Kevin. Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws.. United States: N. p., 2019. Web. doi:10.2172/1569346.
Lee, Kookjin, Lee, Kookjin, Lee, Kookjin, Lee, Kookjin, Carlberg, Kevin, & Carlberg, Kevin. Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws.. United States. doi:10.2172/1569346.
Lee, Kookjin, Lee, Kookjin, Lee, Kookjin, Lee, Kookjin, Carlberg, Kevin, and Carlberg, Kevin. Sun . "Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws.". United States. doi:10.2172/1569346. https://www.osti.gov/servlets/purl/1569346.
@article{osti_1569346,
title = {Deep Conservation: A latent dynamics model for exact satisfaction of physical conservation laws.},
author = {Lee, Kookjin and Lee, Kookjin and Lee, Kookjin and Lee, Kookjin and Carlberg, Kevin and Carlberg, Kevin},
abstractNote = {Abstract not provided.},
doi = {10.2172/1569346},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}