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LUND UNIVERSITY LIBRARIES

Reaching for the limit of stability

Cui, Weiyi LU (2020) FYSM30 20201
Mathematical Physics
Department of Physics
Abstract
In this project, calculations for the total binding energy of all even-even nuclei available from experimental data are performed using the HFBTHO program. An artificial neural network is applied to train the information obtained from the HFBTHO calculations and predict the binding energy for the nuclei. The results show
impressive improvements to the HFBTHO program. In recent years, the combination of
scientific research and machine learning algorithms has become a popular and successful practice. Although it is hard to judge whether an algorithm is good enough, especially with the rapid development of computer science, the application of machine learning in nuclear models can be reliable and promising in predicting the nuclear... (More)
In this project, calculations for the total binding energy of all even-even nuclei available from experimental data are performed using the HFBTHO program. An artificial neural network is applied to train the information obtained from the HFBTHO calculations and predict the binding energy for the nuclei. The results show
impressive improvements to the HFBTHO program. In recent years, the combination of
scientific research and machine learning algorithms has become a popular and successful practice. Although it is hard to judge whether an algorithm is good enough, especially with the rapid development of computer science, the application of machine learning in nuclear models can be reliable and promising in predicting the nuclear properties. (Less)
Popular Abstract
Nowadays, artificial intelligence (AI) is participating more and more in people’s daily life, even though some do not realize that. From speech recognition to graph classification, the algorithms of machine learning (ML) has reached a quite trustworthy level. However, if we apply the ML methods into research in nuclear physics, what will happen?
Please use this url to cite or link to this publication:
author
Cui, Weiyi LU
supervisor
organization
course
FYSM30 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
nuclear physics, HFBTHO, DFT, machine learning, artificial neural network
language
English
id
9027130
date added to LUP
2020-08-26 08:09:33
date last changed
2020-08-26 08:09:56
@misc{9027130,
  abstract     = {{In this project, calculations for the total binding energy of all even-even nuclei available from experimental data are performed using the HFBTHO program. An artificial neural network is applied to train the information obtained from the HFBTHO calculations and predict the binding energy for the nuclei. The results show
impressive improvements to the HFBTHO program. In recent years, the combination of
scientific research and machine learning algorithms has become a popular and successful practice. Although it is hard to judge whether an algorithm is good enough, especially with the rapid development of computer science, the application of machine learning in nuclear models can be reliable and promising in predicting the nuclear properties.}},
  author       = {{Cui, Weiyi}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Reaching for the limit of stability}},
  year         = {{2020}},
}