Physics-Embedded Bayesian Neural Network Developed for Predicting Energy-Dependent Fission Product Yields
Researchers have developed a physics-embedded Bayesian neural network (PE-BNN) framework capable of predicting how fission product yields vary with neutron energy, including fine structural features. The model integrates prior nuclear physics knowledge—specifically an energy-independent phenomenological shell factor—as a direct input, combined with hyperparameter optimization using the Watanabe-Akaike Information Criterion. This approach could improve the accuracy of nuclear data libraries used in reactor design, nuclear waste management, and nonproliferation applications.
A team of nuclear physicists has introduced a PE-BNN framework that merges machine learning with established nuclear physics principles to predict fission product yield (FPY) distributions as a function of incident neutron energy. Rather than treating the neural network as a purely data-driven black box, the researchers embed a phenomenological shell factor—capturing the influence of nuclear shell structure on fission fragment distributions—as a single physics-informed input feature. This design choice allows the model to reproduce both the broad global energy trends and the fine structures, such as odd-even staggering and shell-closure effects, that are characteristic of experimental FPY data. Hyperparameter tuning via the WAIC criterion further sharpens predictive performance while providing Bayesian uncertainty quantification. The framework demonstrates close agreement with known shell effects and prompt neutron multiplicities across the energy range studied. The work is presented as an 8-page paper with 10 figures, submitted to arXiv in April 2025 and revised through June 2026, with a related peer-reviewed DOI indicating journal publication. Accurate FPY predictions are critical for nuclear reactor simulations, antineutrino spectrum calculations, and the assessment of radioactive waste inventories.
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- arXiv physicsCenter
A physics-embedded Bayesian neural network for predicting the energy dependence of fission product yields with fine structures
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