$Adaptive sampling for accelerating neutron diffraction-based strain mapping This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive,paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U$

Adaptive sampling for accelerating neutron diffraction-based strain mapping This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive,paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U

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Veröffentlicht in:	Machine learning: science and technology 2023-04, Vol.4 (2)
Hauptverfasser:	Venkatakrishnan, S V, Fancher, Chris M, Ziatdinov, Maxim, Vasudevan, Rama, Saleeby, Kyle, Haley, James, Yu, Dunji, An, Ke, Plotkowski, Alex
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayesian optimization Gaussian process regression neutron diffraction strain mapping
Online-Zugang:	Volltext
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ISSN:	2632-2153
DOI:	10.1088/2632-2153/acc512