Deep Learning Application for Milne problem with linear anisotropic scattering

Using deep learning algorithms, which is a sub-branch of artificial intelligence, in this study, a deep learning model is developed according to both the number of secondary neutrons and the linear anisotropic scattering coefficient. These are independent variables that the dependent variable is the extrapolation distance. The training data set was calculated with HN method. ANN algorithm was written by TensorFlow and Keras which are the modules in Python programming language. The performance of the deep learning model for this problem has high performance so that the predicted new data which doesn’t be in the training data set is reliable according to the success of the model.

Authors

Institutions

Publication Details

Journal
GAZI UNIVERSITY JOURNAL OF SCIENCE
Published
2024-12-29
DOI
https://doi.org/10.35378/gujs.1521834
Citations
1
Primary Topic
Electromagnetic Scattering and Analysis
Type
article
Field-Weighted Citation Impact
0.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deep Learning Application for Milne problem with linear anisotropic scattering

R. Gökhan Türeci
1 citations
GAZI UNIVERSITY JOURNAL OF SCIENCE
Electromagnetic Scattering and Analysis
0.34
article

Deep Learning Application for Milne problem with linear anisotropic scattering

R. Gökhan Türeci
article en
1 citations

Abstract

Using deep learning algorithms, which is a sub-branch of artificial intelligence, in this study, a deep learning model is developed according to both the number of secondary neutrons and the linear anisotropic scattering coefficient. These are independent variables that the dependent variable is the extrapolation distance. The training data set was calculated with HN method. ANN algorithm was written by TensorFlow and Keras which are the modules in Python programming language. The performance of the deep learning model for this problem has high performance so that the predicted new data which doesn’t be in the training data set is reliable according to the success of the model.

GAZI UNIVERSITY JOURNAL OF SCIENCEVol. 38(2)
Kırıkkale University (TR)
Openalex Percentile: Top 37%
Electromagnetic Scattering and Analysis
0.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.