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Title: Machine Learning

Abstract

Presentation overview: Why Machine Learning?; Why ML-Informed Physics?; Why Physics-Informed ML?; LANL ML strategy related to National & Energy Security; ML-Informed Physics for climate modeling and induced seismicity (LDRD-DR); Physics-Informed ML for blind source separation (LDRD-DR); ML forecasting production from tight rock reservoirs (Chevron)

Authors:
ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1492563
Report Number(s):
LA-UR-19-20140
DOE Contract Number:  
89233218CNA000001
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
Earth Sciences; Information Science; Mathematics; tensor factorization; matrix factorization; unsupervised machine learning

Citation Formats

Vesselinov, Velimir Valentinov. Machine Learning. United States: N. p., 2019. Web. doi:10.2172/1492563.
Vesselinov, Velimir Valentinov. Machine Learning. United States. doi:10.2172/1492563.
Vesselinov, Velimir Valentinov. Thu . "Machine Learning". United States. doi:10.2172/1492563. https://www.osti.gov/servlets/purl/1492563.
@article{osti_1492563,
title = {Machine Learning},
author = {Vesselinov, Velimir Valentinov},
abstractNote = {Presentation overview: Why Machine Learning?; Why ML-Informed Physics?; Why Physics-Informed ML?; LANL ML strategy related to National & Energy Security; ML-Informed Physics for climate modeling and induced seismicity (LDRD-DR); Physics-Informed ML for blind source separation (LDRD-DR); ML forecasting production from tight rock reservoirs (Chevron)},
doi = {10.2172/1492563},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Technical Report:

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