A statistical learning approach for the design of polycrystalline materials

DOI10.1002/sam.10017OpenAlexW3217564632MaRDI QIDQ4969635

Nicholas Zabaras, Veera Sundararaghavan

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2027.42/62057

zbMATH Keywords

materials-by-designadaptive proper orthogonal decompositionmicrostructure databaseX-means classification

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Cites Work

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• Design across length scales: a reduced-order model of polycrystal plasticity for the control of microstructure-sensitive material properties
• Adaptive Reduced-Order Controllers for a Thermal Flow System Using Proper Orthogonal Decomposition
• Bayes Factors

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