论文标题
从输入层中提供贝叶斯神经网络中的物理指南：巨型偶极共振预测的情况
Providing physics guides in Bayesian neural networks from input layer: case of giant dipole resonance predictions
论文作者
论文摘要
贝叶斯神经网络（BNN）已应用于评估和预测核数据。但是，如何在BNN中提供物理指南是关键，但是一个悬而未决的问题。在这项工作中，提出了有关巨型偶极共振（GDR）能量的案例研究，以说明该方法从输入层中提供BNN物理指南的方法的有效性和操作性。 Spearman的相关系数用于评估基态和GDR能量的核性质之间的统计依赖性。然后将最佳地基特性用作BNN中的输入层来评估和预测GDR能量。那些选定的地面属性积极地有助于减少预测错误并避免非物理差异的风险。这项工作提供了一个演示，可以通过使用BNN而没有物理动机模型来找到GDR能量的影响，这可能有助于从复杂的核数据中发现物理效应。
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.
