  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 demonstrated
significant predictive accuracy, 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.
