[Submitted on 30 Jul 2024]
From Complexity to Clarity: Kolmogorov-Arnold Networks in Nuclear Binding Energy Prediction
Hao Liu · Jin Lei · Zhongzhou Ren
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-Weizsäcker 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.
- Comments:
- accepted by Phys. Rev. C
- Subjects:
- Nuclear Theory (nucl-th)
- arXiv:
- 2407.20737 [pdf]