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Optimal Design of Experiment for X-Ray Spectromicroscopy by Machine Learning

Published online by Cambridge University Press:  10 August 2018

Tetsuro Ueno ,
Hideitsu Hino  and
Kanta Ono
Tetsuro Ueno*
Affiliation:
Quantum Beam Science Research Directorate, National Institutes for Quantum and Radiological Science and Technology, Sayo, Japan
Hideitsu Hino
Affiliation:
The Institute of Statistical Mathematics, Tachikawa, Japan
Kanta Ono
Affiliation:
Institute of Materials Structure Science, High Energy Accelerator Research Organization, Tsukuba, Japan
*
*Corresponding author, ueno.tetsuro@qst.go.jp

Abstract

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Type
Abstract
Information
Microscopy and Microanalysis , Volume 24 , Supplement S2: Proceedings of the 14th International Conference on X-ray Microscopy (XRM2018) , August 2018 , pp. 134 - 135
Copyright
© Microscopy Society of America 2018 

References

References:

[1] Ueno, T, et al, npj Comput. Mater 4 2018 4.Google Scholar
[2] Stavitski, E de Groot, F M F Micron 41 2010 687.Google Scholar
[3] Roustant, O, Ginsbourger, D Deville, Y J. Stat. Softw. 51 2012 1.Google Scholar
[4] HH acknowledges the support from CREST (No. JPMJCR1761) from Japan Science and Technology Agency (JST)..Google Scholar