Systems and Means of Informatics
2022, Volume 32, Issue 2, pp 13-22
MACHINE LEARNING FOR EXTRAPOLATION PROBLEMS WITH SMALL DATASET
- A. O. Belozerov
- A. I. Mazur
Abstract
A new method of extrapolation of variational calculations which is based on training a large number of artificial neural networks with subsequent filtering to select the best networks is proposed. This method is used to evaluate ground state energy for model problem and to calculate the ground state energy of a 4He nucleus based on calculations in no-core shell model with realistic potential Daejeon16. The results convergence with increasing number of input data was studied. It is shown that the method allows one to achieve a sufficiently high prediction accuracy even in the case of small model spaces. The method can be applied both in finding various characteristics of nuclei and in solving other problems not related to nuclear physics.
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[+] About this article
Title
MACHINE LEARNING FOR EXTRAPOLATION PROBLEMS WITH SMALL DATASET
Journal
Systems and Means of Informatics
Volume 32, Issue 2, pp 13-22
Cover Date
2022-06-10
DOI
10.14357/08696527220202
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
machine learning; extrapolation methods; ground state energy; nuclear shell model
Authors
A. O. Belozerov  and A. I. Mazur
Author Affiliations
Pacific National University, 136 Tikhookeanskaya Str., Khabarovsk 680035, Russian Federation
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