| 6 | Data analytics using canonical correlation analysis and Monte Carlo simulation显示文摘A canonical correlation analysis is a generic parametric model used in the statistical analysis of data involving interrelated or interdependent input and output variables.It is especially useful in data analytics as a dimensional reduction strategy that simplifies a complex,multidimensional parameter space by identifying a relatively few combinations of variables that are maximally correlated.One shortcoming of the canonical correlation analysis,however,is that it provides only a linear combination of variables that maximizes these correlations.With this in mind,we describe here a versatile,Monte-Carlo based methodology that is useful in identifying non-linear functions of the variables that lead to strong input/output correlations.We demonstrate that our approach leads to a substantial enhancement of correlations,as illustrated by two experimental applications of substantial interest to the materials science community,namely:(1)determining the interdependence of processing and microstructural variables associated with doped polycrystalline aluminas,and(2)relating microstructural decriptors to the electrical and optoelectronic properties of thin-film solar cells based on CuInSe_(2) absorbers.Finally,we describe how this approach facilitates experimental planning and process control. | Jeffrey M.Rickman Yan Wang Anthony D.Rollett Martin P.Harmer Charles Compson | 2017 | npj Computational Materials2017，，1： | 0 |
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