论文标题
通过混乱的方法来表征相位过渡的方法
A learning by confusion approach to characterize phase transitions
论文作者
论文摘要
最近，已经提出了通过混淆（LBC）方法作为机器学习工具来确定相变的临界温度TC，而没有任何先验知识的近似值。但是，该方法的有效性仅用于连续的相变，即仅由故意的错误标记数据引起混乱，而不是由于不同阶段的共存而导致的。为了验证混淆方案是否也可以用于不连续的相变，在这项工作中，我们将LBC方法应用于三种微观模型，即Blume-Capel，Q-State Potts和Falicov-Kimball模型，该模型根据模型参数进行了连续或连续的相变。借助一个简单的模型，我们预测不连续的相变中存在的相共存会使神经网络更加困惑，从而降低其性能。但是，对上述模型执行的数值计算表明，这种相变的其他方面更重要，并且可以使LBC方法降低效率。然而，我们证明，在某些情况下，相同的方面使我们能够使用LBC方法来识别相变的顺序
Recently, the learning by confusion (LBC) approach has been proposed as a machine learning tool to determine the critical temperature Tc of phase transitions without any prior knowledge of its even approximate value. However, the effectiveness of the method has been demonstrated only for continuous phase transitions, where confusion can result only from a deliberate incorrect labeling of the data and not from the coexistence of different phases. To verify whether the confusion scheme can also be used for discontinuous phase transitions, in this work, we apply the LBC method to three microscopic models, the Blume-Capel, the q-state Potts, and the Falicov-Kimball models, which undergo continuous or discontinuous phase transitions depending on model parameters. With the help of a simple model, we predict that the phase coexistence present in discontinuous phase transitions can make the neural network more confused and thus decrease its performance. However, numerical calculations performed for the models mentioned above indicate that other aspects of this kind of phase transition are more important and can render the LBC method less effective. Nevertheless, we demonstrate that in some cases the same aspects allow us to use the LBC method to identify the order of a phase transition
