论文标题
基于Kohn-Sham计划的核系统神经网络
A Kohn-Sham Scheme Based Neural Network for Nuclear Systems
论文作者
论文摘要
基于Kohn-Sham计划的多任务神经网络详细阐述了核壳进化的监督学习。训练集由320个核的单粒子波函数和占用概率组成，由Skyrme密度功能理论计算得出。发现推导的密度分布，动量分布和电荷半径与未经训练的核的基准测试结果达成了良好的协议。特别是，完成壳演化会导致核密度外推的显着改善。经过进一步的基于电荷的校准后，网络发展出更强的预测能力。这通过结合核复合系统的实验数据来推断可观察结果之间相关性的可能性。
A Kohn-Sham scheme based multi-task neural network is elaborated for the supervised learning of nuclear shell evolution. The training set is composed of the single-particle wave functions and occupation probabilities of 320 nuclei, calculated by the Skyrme density functional theory. It is found that the deduced density distributions, momentum distributions, and charge radii are in good agreements with the benchmarking results for the untrained nuclei. In particular, accomplishing shell evolution leads to a remarkable improvement in the extrapolation of nuclear density. After a further charge-radius-based calibration, the network evolves a stronger predictive capability. This opens the possibility to infer correlations among observables by combining experimental data for nuclear complex systems.
