Application of deep neural networks for high-dimensional large BWR core neutronics
Authors:
- Rabie Abu SaleemRSRabie Abu Saleem
- Majdi I. RadaidehMRMajdi I. Radaideh
- Tomasz KozlowskiTKTomasz Kozlowski
Open Access
- 1 December 2020
- journal article
- research article
- Published by Elsevier in Nuclear Engineering and Technology
- Vol. 52 (12) , 2709-2716
Abstract
No abstract availableKeywords
This publication has 18 references indexed in Scilit:
- Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural NetworkScience and Technology of Nuclear Installations, 2016
- Deep learningNature, 2015
- Core loading pattern optimization of a typical two-loop 300MWe PWR using Simulated Annealing (SA), novel crossover Genetic Algorithms (GA) and hybrid GA(SA) schemesAnnals of Nuclear Energy, 2014
- Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural networkAnnals of Nuclear Energy, 2012
- Estimation of research reactor core parameters using cascade feed forward artificial neural networksProgress in Nuclear Energy, 2009
- Local power peaking factor estimation in nuclear fuel by artificial neural networksAnnals of Nuclear Energy, 2009
- Numerical simulation and artificial neural network modeling of natural circulation boiling water reactorNuclear Engineering and Design, 2006
- Using a multi-state recurrent neural network to optimize loading patterns in BWRsAnnals of Nuclear Energy, 2004
- Application of artificial neural network for safety core parameters prediction in LWRRSProgress in Nuclear Energy, 2004
- Nuclear fuel loading pattern optimisation using a neural networkAnnals of Nuclear Energy, 2003