Optimizing multilayer Bayesian neural networks for evaluation of fission yields

Abstract

The Bayesian machine learning is a promising tool for the evaluation of nuclear fission data but its potential capability has not been fully realized. We attempt to optimize the performances of the multilayer Bayesian neural networks for evaluations of fission yields. The influences of adjustments of learning data, activation functions, network structures have been studied. In particular, negative values of net functions have been penalized to avoid non-physical inferences of fission yields. Presently the network with double hidden layers has optimal performances compared to the single-layer or deeper networks. These studies are essential for further developments of precise evaluation methods.

5 pages and 5 figures. arXiv admin note: text overlap with arXiv:2102.09314

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Cited by in corpus (7)

• Machine Learning in Nuclear Physics
• Nuclear masses learned from a probabilistic neural network
• Decay of superheavy nuclei based on the random forest algorithm
• Bayesian Data Fusion of Imperfect Fission Yields for Augmented Evaluations
• Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate Energies
• Hybrid neural network method of a multilayer perceptron and autoencoder for the α-particle preformation factor in α-decay theory
• Generation of fission yield covariance matrices and its application in uncertainty analysis of decay heat