This part employ a code to read parameters of the trained Bayesian neural network which are stored in the log file, and output the averaged function values and Confidence Interval (CI) of fission yields. The instructions are given in the attached BNN-fission.PDF file
International audienceThe study of fission yields has a major impact on the characterization and und...
Having accurate measurements of fission observables is important for a variety of applications, rang...
Bayesian networks were recently suggested as a framework for nuclear data evaluation. Their theory w...
We demonstrate that Bayesian machine learning can be used to treat the vast amount of experimental f...
A systematic study based on the Bayesian Neural Network (BNN) statistical approach is introduced to ...
The JEFF library does not contain fission yield covariances, but simply best estimates and uncertain...
In the present work, we analyze how fission yields uncertainties can be propagated in a burn-up calc...
The JEFF library does not contain fission yield covariances, but simply best estimates and uncertain...
A considerable support has been given in URANIE utilization by the research group of DEN/DER/SPRC/LE...
As machine learning methods gain traction in the nuclear physics community, especially those methods...
Nuclear data are widely used in many research fields. In particular, neutron-induced reaction cross ...
The study of fission yields has a major impact on the characterization and understanding of the fiss...
Fission product yields are fundamental parameters for several nuclear engineering calculations and i...
none5siRecent needs for more accurate fission product yields include covariance information to allow...
International audienceScenario studies simulate the whole fuel cycle over a period of time, from ext...
International audienceThe study of fission yields has a major impact on the characterization and und...
Having accurate measurements of fission observables is important for a variety of applications, rang...
Bayesian networks were recently suggested as a framework for nuclear data evaluation. Their theory w...
We demonstrate that Bayesian machine learning can be used to treat the vast amount of experimental f...
A systematic study based on the Bayesian Neural Network (BNN) statistical approach is introduced to ...
The JEFF library does not contain fission yield covariances, but simply best estimates and uncertain...
In the present work, we analyze how fission yields uncertainties can be propagated in a burn-up calc...
The JEFF library does not contain fission yield covariances, but simply best estimates and uncertain...
A considerable support has been given in URANIE utilization by the research group of DEN/DER/SPRC/LE...
As machine learning methods gain traction in the nuclear physics community, especially those methods...
Nuclear data are widely used in many research fields. In particular, neutron-induced reaction cross ...
The study of fission yields has a major impact on the characterization and understanding of the fiss...
Fission product yields are fundamental parameters for several nuclear engineering calculations and i...
none5siRecent needs for more accurate fission product yields include covariance information to allow...
International audienceScenario studies simulate the whole fuel cycle over a period of time, from ext...
International audienceThe study of fission yields has a major impact on the characterization and und...
Having accurate measurements of fission observables is important for a variety of applications, rang...
Bayesian networks were recently suggested as a framework for nuclear data evaluation. Their theory w...
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