Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data

Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data

Over the past decade, a variety of innovative methodologies have been developed to better characterize the relationships between processing conditions and the physical, morphological, and chemical features of special nuclear material (SNM). Different processing conditions generate SNM products with...

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Podrobná bibliografie
Hlavní autoři: Audrey Lamson McCombs, Madeline Anne Stricklin, Katherine Goode, J. Gabriel Huerta, Kurtis Shuler, J. Derek Tucker, Adah Zhang, Lucas Sweet, Daniel Ries
Médium: Článek
Jazyk:English
Vydáno: Frontiers Media S.A. 2024-05-01
Edice:Frontiers in Nuclear Engineering
Témata:
Bayesian analysis
functional data analysis
inverse prediction
nuclear engineering
nuclear forensics
seemingly unrelated regression
On-line přístup:https://www.frontiersin.org/articles/10.3389/fnuen.2024.1331349/full

Internet

https://www.frontiersin.org/articles/10.3389/fnuen.2024.1331349/full

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