Deep Learning Application for Milne problem with linear anisotropic scattering

R. Gökhan Türeci

Makale detayları

Deep Learning Application for Milne problem with linear anisotropic scattering

Yazarlar:

R. Gökhan Türeci

Yayıncı:

Gazi University

Ana Başlıklar:

Artificial Intelligence (Other) | Nuclear and Plasma Physics (Other)

Dergi:

Gazi University Journal of Science Vol.38 Issue 2, pp.986-994

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001348410

pISSN:

2147-1762

Anahtar kelimeler:

Özet:

 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 doesnt be in the training data set is reliable according to the success of the model.

 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 doesnt be in the training data set is reliable according to the success of the model.

 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 doesnt be in the training data set is reliable according to the success of the model.

 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 doesnt be in the training data set is reliable according to the success of the model.

Makale detayları

Deep Learning Application for Milne problem with linear anisotropic scattering

Yazarlar:

R. Gökhan Türeci

Yayıncı:

Gazi University

Ana Başlıklar:

Artificial Intelligence (Other) | Nuclear and Plasma Physics (Other)

Dergi:

Gazi University Journal of Science Vol.38 Issue 2, pp.986-994

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001348410

pISSN:

2147-1762

Deep Learning

Milne problem

Linear anisotropic scattering

Artificial neural networks

Sayı

Makale detayları

Deep Learning Application for Milne problem with linear anisotropic scattering

Yazarlar: R. Gökhan Türeci

Yayıncı: Gazi University

Ana Başlıklar: Artificial Intelligence (Other) | Nuclear and Plasma Physics (Other)

Dergi: Gazi University Journal of Science Vol.38 Issue 2, pp.986-994

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001348410

pISSN: 2147-1762

Deep Learning

Milne problem

Linear anisotropic scattering

Artificial neural networks

Sayı

Yazarlar:

R. Gökhan Türeci

Yayıncı:

Gazi University

Ana Başlıklar:

Artificial Intelligence (Other) | Nuclear and Plasma Physics (Other)

Dergi:

Gazi University Journal of Science Vol.38 Issue 2, pp.986-994

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001348410

pISSN:

2147-1762