Research on neutron-induced fission product yields of \(^{232}\) Th is crucial for understanding the competition between symmetric and asymmetric fission in actinide nuclei. However, obtaining complete isotopic yield distributions over a wide range of neutron energies remains a challenge. In this study, a Bayesian neural network model was developed to predict the independent (IND) and cumulative fission yields of \(^{232}\) Th under neutron irradiation at various incident energies. To address the limited availability of experimental data for the analysis of IND mass distributions, we substituted mass-number-based yields with the yields of specific isotopes. Furthermore, physical phenomena or quantities, such as the odd–even effect and isospin, were introduced as constraints to enhance the physical consistency of the predictions. The impact of these constraints was evaluated using mass-chain yield distributions and their dependence on energy. Incorporating physical constraints significantly improves the prediction accuracy, yielding more reliable and physically meaningful fission yield data for nuclear physics and reactor design applications.
