English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The Bayesian neural network (BNN) has been applied to evaluate and predictthe nuclear data. However, how to provide physics guides in BNN is a key but anopen question. In this work, the case study on giant dipole resonance (GDR)energy is presented to illustrate the effectiveness and maneuverability of themethod to provide physics guides in BNN from input layer. The Spearman'scorrelation coefficients are applied to assess the statistical dependencebetween nuclear properties in the ground state and the GDR energies. Then theoptimal ground-state properties are employed as the input layer in the BNN forevaluating and predicting the GDR energies. Those selected ground-stateproperties actively contributes to reduce the predicted errors and avoid therisk of the non-physics divergence. This work gives a demonstration to findeffects of the GDR energy by using the BNN without the physics motivated model,which may be helpful for discovering physics effects from the complex nucleardata.

Providing physics guides in Bayesian neural networks from input layer: case of giant dipole resonance predictions