From Complexity to Clarity: Kolmogorov-Arnold Networks in Nuclear Binding Energy Prediction
Abstract
This study explores the application of Kolmogorov-Arnold Networks (KANs) in predicting nuclear binding energies, leveraging their ability to decompose complex multi-parameter systems into simpler univariate functions. By utilizing data from the Atomic Mass Evaluation (AME2020) and incorporating features such as atomic number, neutron number, and shell effects, KANs achieved a significant lower root mean square error (0.26~MeV), surpassing traditional models. The symbolic regression analysis yielded simplified analytical expressions for binding energies, aligning with classical models like the liquid drop model and the Bethe-Weizs\"acker formula. These results highlight KANs' potential in enhancing the interpretability and understanding of nuclear phenomena, paving the way for future applications in nuclear physics and beyond.
Keywords
Cite
@article{arxiv.2407.20737,
title = {From Complexity to Clarity: Kolmogorov-Arnold Networks in Nuclear Binding Energy Prediction},
author = {Hao Liu and Jin Lei and Zhongzhou Ren},
journal= {arXiv preprint arXiv:2407.20737},
year = {2025}
}
Comments
accepted by Phys. Rev. C