Machine learning-assisted surrogate construction for full-core fuel performance analysis-Reference-Cited by-同舟云学术

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Machine learning-assisted surrogate construction for full-core fuel performance analysis

Published:2022-04 Issue: Volume:168 Page:108905
ISSN:0306-4549
Container-title:Annals of Nuclear Energy
language:en
Short-container-title:Annals of Nuclear Energy

Author:

Che Yifeng,Yurko Joseph,Seurin Paul,Shirvan Koroush

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference31 articles.

1. Advanced LWR nuclear fuel cladding system development trade-off study;Barrett;Idaho National Laboratory (INL),2012

2. Full core LOCA safety analysis for a PWR containing high burnup fuel;Capps,2020

3. The PROMETRA program: fuel cladding mechanical behavior under high strain rate;Cazalis;Nucl. Technol.,2007

4. Xgboost: A scalable tree boosting system;Chen,2016

5. Chollet, F., 2015, “others.(2015). Keras. GitHub.” 2007.

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on optimization of key thermal parameters of the secondary loop of PWR based on improved BP neural network;Annals of Nuclear Energy;2024-07

2. Recovering From Structural Instability in Nuclear Power Design Optimization;Nuclear Science and Engineering;2024-02-27

3. An overview of power reactor kinetics and control in load-following operation modes;Frontiers in Energy Research;2023-02-23

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