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most citedManifold learning-based polynomial chaos expansions for high-dimensional surrogate models

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Data-driven inverse uncertainty quantification: application to the Chemical Vapor Deposition Reactor Modeling

Geremy Loachamín, Eleni D. Koronaki, Dimitrios G. Giovanis +4

This study presents a Bayesian framework for (inverse) uncertainty quantification and parameter estimation in a two-step Chemical Vapor Deposition coating process using production…