Prediction of burn‐up nucleus density based on machine learning-Reference-Cited by-同舟云学术

Prediction of burn‐up nucleus density based on machine learning

Published:2021-03-28 Issue:9 Volume:45 Page:14052-14061
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Int J Energy Res

Author:

Lei Ji‐Chong¹²^ORCID,Zhou Jian‐Dong³,Zhao Ya‐Nan¹²,Chen Zhen‐Ping¹²,Zhao Peng‐Cheng¹²,Xie Chao¹²,Ni Zi‐Ning¹²,Yu Tao¹²,Xie Jin‐Sen¹²

Affiliation:

1. School of Nuclear Science and Technology University of South China Hengyang Hunan China

2. Virtual Simulation Experiment Teaching Center on Nuclear Energy and Technology University of South China Hengyang Hunan China

3. Shanghai Nuclear Engineering Research and Design Institute Co., Ltd Shanghai China

Publisher

Wiley

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.6660

Reference20 articles.

1. General solution of Bateman equations for nuclear transmutations;Jerzy C;Ann Nucl Energy,2006

2. Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization

3. PusaM.Accuracy considerations for Chebyshev rational approximation method (CRAM) in Burnup calculations. Paper presented at: International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering;2013. pp. 973‐984.

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