Evaluation of U-235 and U-238 Fission Product Yields Using Bayesian Neural Networks: Comparison of Baseline and Physics-Informed Models

Abstract

U-235 and U-238 are fundamental materials in thermal and fast neutron breeding studies. Accurate evaluation of their fission product yields is of critical importance for advanced reactor design and nuclear waste management. In this work, a baseline Bayesian neural network model (BNN0) with two hidden layers of 20 neurons each was constructed. An improved model, BNN3, was developed by incorporating additional physics-informed features, namely the odd-even effect, beta-decay energy, and isospin, into the network inputs. Comparative analyses of the general distributions of the fission yields and isotopic chain structures demonstrate that BNN3 exhibits significantly improved reconstruction accuracy and consistency with the target cumulative fission-yield distributions. For 16 representative fission products, the energy-dependent yield predictions of BNN3 show better agreement with both experimental data and evaluated libraries, accompanied by noticeably narrower confidence intervals. These results indicate that the incorporation of relevant physical information improves the model's sensitivity to underlying fission mechanisms and enhances its capability to reproduce the systematic characteristics of cumulative fission-yield distributions. Together, these strategies contribute to more accurate and robust nuclear data modeling, providing a methodological foundation for the evaluation and development of next-generation nuclear data libraries.