G-HyND: a hybrid nuclear data estimator with Gaussian processes
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2021/10/18
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Indian UGC (journal)
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Refrences37
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Citations5
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Hiroki Iwamoto Nuclear Science and Engineering Center, Japan Atomic Energy Agency, Ibaraki, Japan ORCID (unauthenticated)
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Osamu Iwamoto Nuclear Science and Engineering Center, Japan Atomic Energy Agency, Ibaraki, Japan
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Satoshi Kunieda Nuclear Science and Engineering Center, Japan Atomic Energy Agency, Ibaraki, Japan
Cite
Iwamoto, Hiroki, et al. “G-HyND: A Hybrid Nuclear Data Estimator With Gaussian Processes”. Journal of Nuclear Science and Technology, vol. 59, no. 3, 2021, pp. 334-4, https://doi.org/10.1080/00223131.2021.1971120.
Iwamoto, H., Iwamoto, O., & Kunieda, S. (2021). G-HyND: a hybrid nuclear data estimator with Gaussian processes. Journal of Nuclear Science and Technology, 59(3), 334-344. https://doi.org/10.1080/00223131.2021.1971120
Iwamoto, Hiroki, Osamu Iwamoto, and Satoshi Kunieda. “G-HyND: A Hybrid Nuclear Data Estimator With Gaussian Processes”. Journal of Nuclear Science and Technology 59, no. 3 (2021): 334-44. https://doi.org/10.1080/00223131.2021.1971120.
Iwamoto H, Iwamoto O, Kunieda S. G-HyND: a hybrid nuclear data estimator with Gaussian processes. Journal of Nuclear Science and Technology. 2021;59(3):334-4.
Journal Categories
Technology
Electrical engineering
Electronics
Nuclear engineering
Technology
Electrical engineering
Electronics
Nuclear engineering
Nuclear engineering
Atomic power
Technology
Engineering (General)
Civil engineering (General)
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Proceedings of the 2015 Symposium on Nuclear Data; 2015 Nov 19–20 | 2016 | |||
| Numerical optimization | 2006 | |||
| Numerical optimization | Radiation Protection Dosimetry |
| 201 | |
| Estimation of uncertainty in lead spallation particle multiplicity and its propagation to a neutron energy spectrum | Journal of Nuclear Science and Technology |
| 2 | 2019 |
| Monte Carlo uncertainty quantification of the effective delayed neutron fraction | Journal of Nuclear Science and Technology |
| 10 | 2018 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Developing a New Web Service for Experimental Nuclear Reaction Database (EXFOR) Using RESTful API and JSON | EPJ Web of Conferences |
| 2024 | |
| Japanese evaluated nuclear data library version 5: JENDL-5 | Journal of Nuclear Science and Technology |
| 98 | 2023 |
Estimation of double-differential cross-sections of 9Be(p,xn) reaction for new nuclear data library JENDL-5 | Journal of Neutron Research |
| 2023 | |
| Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique | Applied Radiation and Isotopes |
| 1 | 2022 |
| Nuclear data generation by machine learning (I) application to angular distributions for nucleon-nucleus scattering | Journal of Nuclear Science and Technology |
| 2022 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Nuclear engineering. Atomic power 4
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique and was published in 2022. The most recent citation comes from a 2024 study titled Developing a New Web Service for Experimental Nuclear Reaction Database (EXFOR) Using RESTful API and JSON. This article reached its peak citation in 2023, with 2 citations. It has been cited in 4 different journals, 25% of which are open access. Among related journals, the Journal of Nuclear Science and Technology cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.