论文标题
机器学习阶段和批判性无需使用真实数据进行培训
Machine learning phases and criticalities without using real data for training
论文作者
论文摘要
我们研究了使用已监督神经网络（NN）的技术，研究了三维（3D）经典O（3）模型和二维（2D）经典XY模型的相变和二维（2D）经典XY模型以及2D和3D二聚体Spin-1/2抗fiferromagnets的量子相变。此外，与文献中常用的常规方法不同，我们调查中使用的训练集既不是理论上的理论，也不是所考虑系统的真实配置。值得注意的是，由于这种非常规的训练阶段建立，并结合了半实验有限尺寸的缩放公式，由NN方法确定的相关临界点与文献中既定的结果非常吻合。这里获得的结果暗示，像本研究中使用的某些非常规培训策略不仅在计算中具有成本效益，而且还适用于野生的物理系统。
We study the phase transitions of three-dimensional (3D) classical O(3) model and the two-dimensional (2D) classical XY model, as well as both the quantum phase transitions of 2D and 3D dimerized spin-1/2 antiferromagnets, using the techniques of supervised neural network (NN). Moreover, unlike the conventional approaches commonly used in the literature, the training sets employed in our investigation are neither the theoretical nor the real configurations of the considered systems. Remarkably, with such an unconventional set up of the training stage in conjunction with semi-experimental finite-size scaling formulas, the associated critical points determined by the NN method agree well with the established results in the literature. The outcomes obtained here imply that certain unconventional training strategies, like the one used in this study, are not only cost-effective in computation, but are also applicable for a wild range of physical systems.
