Providing physics guidance in Bayesian neural networks from the input layer: The case of giant dipole resonance predictions
Providing physics guidance in Bayesian neural networks from the input layer: The case of giant dipole resonance predictions
复制标题
从输入层为贝叶斯神经网络提供物理指导：巨型偶极子共振预测的情况
DOI：
10.1103/physrevc.104.034317
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发表时间：
2020-11
影响因子：
3.1
通讯作者：
Su Jun
中科院分区：
文献类型：
--
作者：
Wang Xiaohang;Zhu Long;Su Jun
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.
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DOI：
10.1007/978-1-4612-0745-0
发表时间：
1995
期刊：
--
影响因子：
--
作者：
Radford M. Neal
通讯作者：
Radford M. Neal
DOI：
10.1016/s0065-2458(08)60404-0
发表时间：
1993
期刊：
Adv. Comput.
影响因子：
--
作者：
Franziska Wulf
通讯作者：
Franziska Wulf
影响因子：
3.1
作者：
L. Neufcourt;Yuchen Cao;W. Nazarewicz;F. Viens
通讯作者：
L. Neufcourt;Yuchen Cao;W. Nazarewicz;F. Viens
DOI：
10.1140/epja/i2019-12854-7
发表时间：
2019-12
期刊：
The European Physical Journal A
影响因子：
--
作者：
N. Kobayashi;K. Miki;T. Hashimoto;C. Iwamoto;A. Tamii;N. Aoi;M. Carpenter;K. Hatanaka;J. Isaak;E. Ideguchi;S. Morinobu;S. Nakamura;S. Noji
通讯作者：
N. Kobayashi;K. Miki;T. Hashimoto;C. Iwamoto;A. Tamii;N. Aoi;M. Carpenter;K. Hatanaka;J. Isaak;E. Ideguchi;S. Morinobu;S. Nakamura;S. Noji
影响因子：
1.9
作者：
KONONENKO, I
通讯作者：
KONONENKO, I