From both the fundamental and applied perspectives, fragment mass distributions are important observablesof fission. We apply the Bayesian neural network (BNN) approach to learn the existing neutron induced fissionyie...From both the fundamental and applied perspectives, fragment mass distributions are important observablesof fission. We apply the Bayesian neural network (BNN) approach to learn the existing neutron induced fissionyields and predict unknowns with uncertainty quantification. Comparing the predicted results with experimentaldata, the BNN evaluation results are found to be satisfactory for the distribution positions and energy dependenciesof fission yields. Predictions are made for the fragment mass distributions of several actinides, which may beuseful for future experiments.展开更多
基金the National Natural Science Foundation of China(12175064,U2167203)the Outstanding Youth Science Foundation of Hunan Province,China(2022JJ10031)。
文摘From both the fundamental and applied perspectives, fragment mass distributions are important observablesof fission. We apply the Bayesian neural network (BNN) approach to learn the existing neutron induced fissionyields and predict unknowns with uncertainty quantification. Comparing the predicted results with experimentaldata, the BNN evaluation results are found to be satisfactory for the distribution positions and energy dependenciesof fission yields. Predictions are made for the fragment mass distributions of several actinides, which may beuseful for future experiments.
