Monte Carlo dropout Bayesian neural network trained with physics inputs reproduces abrupt charge-radii increase near N=60 for Z=37-40 and shell quenching at N=126, achieving comparable RMSD on training and validation sets.
Petrovici, Triple shape coexistence and shape evolution in the N = 58 Sr and Zr isotopes
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Input-driven analysis in predicting nuclear charge radii using Monte Carlo dropout Bayesian neural network
Monte Carlo dropout Bayesian neural network trained with physics inputs reproduces abrupt charge-radii increase near N=60 for Z=37-40 and shell quenching at N=126, achieving comparable RMSD on training and validation sets.