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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| Auteurs principaux: | , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2017
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/45b4ebf2f20e439c90859e0508a28eb2 |
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| Résumé: | 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. |
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