Rational approximations for solving cauchy problems

Turut, Veyis; Bayram, Mustafa

doi:10.20852/ntmsci.2016320380

Cited by 1 publication

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References 16 publications

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“…12 Alternatively, if variable ratios are more appropriate, one may employ a multivariate generalization of Padé approximants. [18][19][20] For example, in the trivariate case, one can write…”

Section: Canonical Correlation Analysismentioning

confidence: 99%

Data analytics using canonical correlation analysis and Monte Carlo simulation

et al. 2017

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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.

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Section: Canonical Correlation Analysismentioning

confidence: 99%