Machine Learning Applications to the One-speed Neutron Transport Problems

R. Gökhan Türeci

doi:10.17776/csj.1163514

EN

Machine Learning Applications to the One-speed Neutron Transport Problems

Abstract

Machine 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.

Keywords

References

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Details

Primary Language

English

Subjects

Classical Physics (Other)

Journal Section

Research Article

Authors

R. Gökhan Türeci ^*
0000-0001-6309-6300
Türkiye

Publication Date

December 27, 2022

Submission Date

August 17, 2022

Acceptance Date

October 26, 2022

Published in Issue

Year 2022 Volume: 43 Number: 4

DOI

https://doi.org/10.17776/csj.1163514

IZ

https://izlik.org/JA37ZD73XG

Cite

RIS / Bibtex

APA

Türeci, R. G. (2022). Machine Learning Applications to the One-speed Neutron Transport Problems. Cumhuriyet Science Journal, 43(4), 726-738. https://doi.org/10.17776/csj.1163514

Machine Learning Applications to the One-speed Neutron Transport Problems

Öz

Machine Learning Applications to the One-speed Neutron Transport Problems

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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