论文标题
POTTS模型与域对抗神经网络的相变研究
Study of phase transition of Potts model with Domain Adversarial Neural Network
论文作者
论文摘要
引入了转移学习方法，域对抗神经网络（DANN），以研究二维Q-State Potts模型的相变。使用DANN，我们只需要自动选择一些标记的配置作为输入数据，然后在训练算法后可以获得关键点。通过额外的迭代过程，可以捕获临界点以与蒙特卡洛模拟相当的精度，因为我们证明了Q = 3、4、5、7和10。还可以同时确定相变（一阶或二阶）的类型。同时，对于Q = 3时的二阶相变，我们可以通过数据崩溃计算关键指数$ν$。此外，与传统的监督学习相比，我们发现Dann的成本较低。
A transfer learning method, Domain Adversarial Neural Network (DANN), is introduced to study the phase transition of two-dimensional q-state Potts model. With the DANN, we only need to choose a few labeled configurations automatically as input data, then the critical points can be obtained after training the algorithm. By an additional iterative process, the critical points can be captured to comparable accuracy to Monte Carlo simulations as we demonstrate it for q = 3, 4, 5, 7 and 10. The type of phase transition (first or second-order) is also determined at the same time. Meanwhile, for the second-order phase transition at q=3, we can calculate the critical exponent $ν$ by data collapse. Furthermore, compared to the traditional supervised learning, we found the DANN to be more accurate with lower cost.
