通过数据扩增对 235U 裂变产物产量能量依赖性的贝叶斯方法

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-05-29 DOI:10.1080/00223131.2024.2361659

Jingde Chen, Yuta Mukobara, Chikako Ishizuka, Tatsuya Katabuchi, Satoshi Chiba

引用次数: 0

摘要

我们利用双隐层贝叶斯神经网络（BNN）模型和数据增强（DA）来预测裂变产物产量（FPY）的能量依赖性。在 BNN 模型中，JENDL-5 ...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bayesian approach to energy dependence of fission product yields of 235U by data augmentation

We utilized a two hidden-layer Bayesian neural network (BNN) model along with data augmentation (DA) to predict the energy dependence of fission product yields (FPY). In the BNN model, the JENDL-5 ...

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.