Machine Learning Applications to the One-speed Neutron Transport Problems

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dc.contributor.authorTüreci, Recep Gökhan
dc.date.accessioned2025-01-21T16:13:08Z
dc.date.available2025-01-21T16:13:08Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractMachine learning is a branch of artificial intelligence and computer science. The purpose of machine learning is to predict new data by using the existing data. In this study, two different machine learning methods which are Polynomial Regression (PR) and Artificial Neural Network (ANN) are applied to the neutron transport problems which are albedo problem, the Milne problem, and the criticality problem. ANN applications contain two different activation functions, Leaky Relu and Elu. The training data set is calculated by using the HN method. PR and ANN results are compared with the literature data. The study is only based on the existing data; therefore, the study could be thought only data mining on the one-speed neutron transport problems for isotropic scattering. 
dc.identifier.doi10.17776/csj.1163514
dc.identifier.endpage738
dc.identifier.issn2587-2680
dc.identifier.issn2587-246X
dc.identifier.issue4
dc.identifier.startpage726
dc.identifier.trdizinid1149883
dc.identifier.urihttps://doi.org/10.17776/csj.1163514
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1149883
dc.identifier.urihttps://hdl.handle.net/20.500.12587/21846
dc.identifier.volume43
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofCumhuriyet Science Journal
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectFizik
dc.subjectUygulamalı
dc.titleMachine Learning Applications to the One-speed Neutron Transport Problems
dc.typeArticle

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