MACHINE LEARNING IN THE PROBLEM OF NO CORE SHELL MODEL RESULT EXTRAPOLATIONS

A method for extrapolating the results of variational calculations to the case of the infinite basis using an ensemble of artificial neural networks is proposed. Extrapolations of the no-core shell model results obtained with nucleon-nucleon interaction Daejeon16 for the ground state energies, as well as for the rootmean-square point-proton, point-neutron and point-nucleon (matter) radii of the Li and He nuclei, are performed.

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Publication Details

Journal
Ядерная физика / Physics of Atomic Nuclei
Published
2025-01-01
DOI
https://doi.org/10.7868/s3034628225010187
Primary Topic
Advanced Data Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

MACHINE LEARNING IN THE PROBLEM OF NO CORE SHELL MODEL RESULT EXTRAPOLATIONS

A. I. Mazur
Ядерная физика / Physics of Atomic Nuclei
Advanced Data Processing Techniques
article

MACHINE LEARNING IN THE PROBLEM OF NO CORE SHELL MODEL RESULT EXTRAPOLATIONS

A. I. Mazur
article en

Abstract

A method for extrapolating the results of variational calculations to the case of the infinite basis using an ensemble of artificial neural networks is proposed. Extrapolations of the no-core shell model results obtained with nucleon-nucleon interaction Daejeon16 for the ground state energies, as well as for the rootmean-square point-proton, point-neutron and point-nucleon (matter) radii of the Li and He nuclei, are performed.

Ядерная физика / Physics of Atomic NucleiVol. 88(1)
Openalex Percentile: Top 64%
Advanced Data Processing Techniques
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MACHINE LEARNING IN THE PROBLEM OF NO CORE SHELL MODEL RESULT EXTRAPOLATIONS — A. I. Mazur · Ядерная физика / Physics of Atomic Nuclei (2025) | TGRS Research Map | TGRS