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• W2951597345 abstract "Fission product yields are key infrastructure data for nuclear applications in many aspects. It is a challenge both experimentally and theoretically to obtain accurate and complete energy-dependent fission yields. We apply the Bayesian neural network (BNN) approach to learn existed fission yields and predict unknowns with uncertainty quantification. We demonstrated that BNN is particularly useful for evaluations of fission yields when incomplete experimental data are available. The BNN results are quite satisfactory on distribution positions and energy dependencies of fission yields." @default.
• W2951597345 created "2019-06-27" @default.
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• W2951597345 date "2019-09-17" @default.
• W2951597345 modified "2023-10-17" @default.
• W2951597345 title "Bayesian Evaluation of Incomplete Fission Yields" @default.
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• W2951597345 doi "https://doi.org/10.1103/physrevlett.123.122501" @default.
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