Machine learning selection of differential and integral experiments to optimally reduce nuclear data uncertainties [Slides]

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Publication Details

Published
2024-12-03
DOI
https://doi.org/10.2172/2537831
Primary Topic
Nuclear reactor physics and engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

Machine learning selection of differential and integral experiments to optimally reduce nuclear data uncertainties [Slides]

Karl J. Friston
Nuclear reactor physics and engineering
article

Machine learning selection of differential and integral experiments to optimally reduce nuclear data uncertainties [Slides]

Karl J. Friston
article en

Abstract

No abstract available for this paper.
U.S. Department of Energy, National Nuclear Security Administration, Laboratory Directed Research and Development, Los Alamos National Laboratory
Openalex Percentile: Top 53%
Nuclear reactor physics and engineering
DOIOpen Access PDFOpenAlex
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Machine learning selection of differential and integral experiments to optimally reduce nuclear data uncertainties [Slides] — Karl J. Friston · (2024) | TGRS Research Map | TGRS