Data analytics using canonical correlation analysis and Monte Carlo simulation

Data analytics: Non-linear model for establishing correlations A method for quantifying non-linear relationships provides insight into the connections between microstructure and properties of materials. Canonical correlation analysis is a common technique used to quantify the relationship between tw...

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Autores principales:	Jeffrey M. Rickman, Yan Wang, Anthony D. Rollett, Martin P. Harmer, Charles Compson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/45b4ebf2f20e439c90859e0508a28eb2
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spelling	oai:doaj.org-article:45b4ebf2f20e439c90859e0508a28eb22021-12-02T15:18:49ZData analytics using canonical correlation analysis and Monte Carlo simulation10.1038/s41524-017-0028-92057-3960https://doaj.org/article/45b4ebf2f20e439c90859e0508a28eb22017-07-01T00:00:00Zhttps://doi.org/10.1038/s41524-017-0028-9https://doaj.org/toc/2057-3960Data analytics: Non-linear model for establishing correlations A method for quantifying non-linear relationships provides insight into the connections between microstructure and properties of materials. Canonical correlation analysis is a common technique used to quantify the relationship between two sets of variables but it is often difficult to apply when the relationships are non-linear. An international team of researchers led by Jeffrey Rickman from Lehigh University now present a Monte-Carlo-based extension of canonical correlation analysis that can be applied to scenarios where non-linear variable dependencies are likely. They demonstrate this approach by establishing correlations between the variables responsible for abnormal grain growth in a ceramic oxide, as well as the variables that are most important in connecting the microstructure to the electrical and optoelectronic properties of certain solar cells, showing the range of materials systems that this approach could be used for.Jeffrey M. RickmanYan WangAnthony D. RollettMartin P. HarmerCharles CompsonNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 3, Iss 1, Pp 1-6 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Jeffrey M. Rickman Yan Wang Anthony D. Rollett Martin P. Harmer Charles Compson Data analytics using canonical correlation analysis and Monte Carlo simulation
description	Data analytics: Non-linear model for establishing correlations A method for quantifying non-linear relationships provides insight into the connections between microstructure and properties of materials. Canonical correlation analysis is a common technique used to quantify the relationship between two sets of variables but it is often difficult to apply when the relationships are non-linear. An international team of researchers led by Jeffrey Rickman from Lehigh University now present a Monte-Carlo-based extension of canonical correlation analysis that can be applied to scenarios where non-linear variable dependencies are likely. They demonstrate this approach by establishing correlations between the variables responsible for abnormal grain growth in a ceramic oxide, as well as the variables that are most important in connecting the microstructure to the electrical and optoelectronic properties of certain solar cells, showing the range of materials systems that this approach could be used for.
format	article
author	Jeffrey M. Rickman Yan Wang Anthony D. Rollett Martin P. Harmer Charles Compson
author_facet	Jeffrey M. Rickman Yan Wang Anthony D. Rollett Martin P. Harmer Charles Compson
author_sort	Jeffrey M. Rickman
title	Data analytics using canonical correlation analysis and Monte Carlo simulation
title_short	Data analytics using canonical correlation analysis and Monte Carlo simulation
title_full	Data analytics using canonical correlation analysis and Monte Carlo simulation
title_fullStr	Data analytics using canonical correlation analysis and Monte Carlo simulation
title_full_unstemmed	Data analytics using canonical correlation analysis and Monte Carlo simulation
title_sort	data analytics using canonical correlation analysis and monte carlo simulation
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/45b4ebf2f20e439c90859e0508a28eb2
work_keys_str_mv	AT jeffreymrickman dataanalyticsusingcanonicalcorrelationanalysisandmontecarlosimulation AT yanwang dataanalyticsusingcanonicalcorrelationanalysisandmontecarlosimulation AT anthonydrollett dataanalyticsusingcanonicalcorrelationanalysisandmontecarlosimulation AT martinpharmer dataanalyticsusingcanonicalcorrelationanalysisandmontecarlosimulation AT charlescompson dataanalyticsusingcanonicalcorrelationanalysisandmontecarlosimulation
_version_	1718387441884200960

Data analytics using canonical correlation analysis and Monte Carlo simulation

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