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Application of Artificial Neural Networks on improving predictions of nuclear radii

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Hellenic Nuclear Physics Society   

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Application of Artificial Neural Networks on improving predictions of nuclear radii (EN)
Mertzimekis, T. J.
Charou, E.
Skarlis, V.
Bratsolis, E.
Artificial Neural Networks (ANN) are mathematical computing paradigms imitating the operations of biological neural systems. Their nonlinear nature and ability to learn from the environment make them highly suited to solve real-world problems from those that are still under development. In the field of Physics there are many problems which cannot be adequately solved with the physics–based methods and the use of ANN may yield better results. In the present work ANNs have been tested in predicting nuclear radii considering as input the atomic and mass numbers, exclusively. The performance of different supervised ANNs is evaluated. The dataset used for the training and testing was based on evaluated data of nuclear radii available in IAEA tables. (EN)
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Artificial Neural Networks (EN)
nuclear charge radii (EN)
Hellenic Nuclear Physics Society
Annual Symposium of the Hellenic Nuclear Physics Society
Annual Symposium of the Hellenic Nuclear Physics Society
English
2019-04-01
https://eproceedings.epublishing.ekt.gr/index.php/hnps/article/view/1796
10.12681/hnps.1796
Hellenic Nuclear Physics Society (HNPS) (EN)
2654-0088
2654-007X
Annual Symposium of the Hellenic Nuclear Physics Society; Τόμ. 26 (2018): HNPS2018; 179-181 (EL)
HNPS Advances in Nuclear Physics; Vol. 26 (2018): HNPS2018; 179-181 (EN)
Πνευματική ιδιοκτησία (c) 2019 V. Skarlis, E. Bratsolis, E. Charou, T. J. Mertzimekis (EL)



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