A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks
A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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For turbulent reacting flows, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal component analysis (PCA), and its variants, is a widely employed class of methods. Recently, an alternative technique...
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A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks
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TN_cdi_proquest_journals_2835322371
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2835322371
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2331-8422
