  The Bayesian neural network (BNN) has been applied to evaluate and predict
the nuclear data. However, how to provide physics guides in BNN is a key but an
open question. In this work, the case study on giant dipole resonance (GDR)
energy is presented to illustrate the effectiveness and maneuverability of the
method to provide physics guides in BNN from input layer. The Spearman's
correlation coefficients are applied to assess the statistical dependence
between nuclear properties in the ground state and the GDR energies. Then the
optimal ground-state properties are employed as the input layer in the BNN for
evaluating and predicting the GDR energies. Those selected ground-state
properties actively contributes to reduce the predicted errors and avoid the
risk of the non-physics divergence. This work gives a demonstration to find
effects of the GDR energy by using the BNN without the physics motivated model,
which may be helpful for discovering physics effects from the complex nuclear
data.
