论文标题
机器学习Ising批判性，并进行旋转
Machine learning of Ising criticality with spin-shuffling
论文作者
论文摘要
我们研究了神经网络在识别与近代最邻居相互作用的2D ISING模型中的关键行为方面的性能。我们将基于DNN和CNN的分类器训练具有最近邻居相互作用的ISING模型配置，并测试模型识别关键（交叉）区域的能力，以及在存在近代最新的邻居相互作用的情况下的阶段。我们的主要目标是研究我们的模型是否可以学习批判性和普遍性的概念，而仅通过简单地学习顺序参数的价值（在这种情况下磁化）来识别阶段。我们设计了一个简单的对抗训练过程，该过程迫使我们的模型在培训时忽略对顺序参数的学习。我们发现，这样的模型能够以合理的成功识别关键区域，这意味着它已经学会了识别临界点附近的自旋旋转相关性的结构。
We investigate the performance of neural networks in identifying critical behaviour in the 2D Ising model with next-to-nearest neighbour interactions. We train DNN and CNN based classifiers on the Ising model configurations with nearest neighbour interactions and test the ability of our models to identify the critical (cross-over) region, as well as the phases in the presence of next-to-nearest neighbour interactions. Our main objective is to investigate whether our models can learn the notion of criticality and universality, in contrast to merely identifying the phases by simply learning the value of the order parameter, magnetization in this case. We design a simple adversarial training process which forces our models to ignore learning about the order parameter while training. We find that such models are able to identify critical region with reasonable success, implying that it has learned to identify the structure of spin-spin correlations near criticality.
