标题： 随机相互作用系综中核基态自旋分布的神经网络研究 显示英文标题

标题： Neural network study on nuclear ground-state spin distribution within random interaction ensemble

摘要： 核基态自旋在两体随机系综（TBRE）中的分布通过使用一个通用分类神经网络（NN）模型进行研究，其中两体相互作用矩阵元作为输入特征，相应的基态自旋作为标签或输出预测。 当涉及到准确预测TBRE中每个样本的基态自旋时，量子多体系统问题似乎超出了我们优化后的神经网络的能力。 然而，我们的神经网络模型有效地捕捉了基态自旋的统计特性。 这可能归因于神经网络（NN）模型已经学习了人类物理学家发现的TBRE中基态自旋分布的经验规律。 显示英文摘要

摘要： The distribution of nuclear ground-state spin in the two-body random ensemble (TBRE) is studied by using a general classification neural network (NN) model with the two-body interaction matrix elements as input features and corresponding ground-state spins as labels or output predictions. It seems that quantum many-body system problem exceeds the capability of our optimized neural networks when it comes to accurately predicting the ground-state spin of each sample within the TBRE. However, our neural network model effectively captures the statistical properties of the ground-state spin. This may be attributed to the fact that the neural network (NN) model has learned the empirical regularity of the ground-state spin distribution in TBRE, as discovered by human physicists.