机器学习阶段和关键性，不使用真实数据进行训练
Machine learning phases and criticalities without using real data for training
摘要：
利用监督神经网络（NN）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，以及二维和三维二聚自旋1/2反铁磁体的量子相变.此外，与文献中常用的传统方法不同，在我们的调查中采用的训练集既不是所考虑系统的理论配置，也不是真实配置。值得注意的是，这样一个非常规的设置结合半实验的有限尺寸标度公式的训练阶段，相关的临界点确定的NN方法同意与文献中的既定结果。这里获得的结果意味着某些非常规的训练策略，如本研究中使用的策略，不仅在计算中具有成本效益，而且还适用于各种物理系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
