InfoCGAN classification of 2D square Ising configurations
InfoCGAN classification of 2D square Ising configurations
复制标题
2D 方形 Ising 配置的 InfoCGAN 分类
DOI：
10.1088/2632-2153/abcc45
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Tam, Ka-Ming
中科院分区：
文献类型：
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作者：
Walker, Nicholas;Tam, Ka-Ming
InfoCGAN神经网络在外部施加的磁场和温度条件下的2D平方伊辛配置上进行训练。该网络由两个主要子网组成。生成器网络学习生成令人信服的伊辛配置，而ESTA网络学习区分“真实的”和“假”配置，并通过辅助网络提供额外的分类分配预测。一些预测的分类分配显示出与伊辛模型中预期的物理相一致，铁磁自旋向上和自旋向下相以及高温弱外场相。此外，与交叉现象相关联的配置预测的模型。分类概率允许一个强大的方法来估计临界温度在消失场的情况下，显示出特殊的协议与已知的物理。这项工作表明，使用对抗性神经网络的表示学习方法可用于识别与物理相非常相似的类别，除了原始物理配置和它们所受的物理条件之外，没有先验信息。适当地实现有限尺寸缩放对于完整的机器学习方法是必不可少的，以便使其符合既定的统计力学，这值得将来研究。
An InfoCGAN neural network is trained on 2D square Ising configurations conditioned on the external applied magnetic field and the temperature. The network is composed of two main sub-networks. The generator network learns to generate convincing Ising configurations and the discriminator network learns to discriminate between'real'and'fake'configurations with an additional categorical assignment prediction provided by an auxiliary network. Some of the predicted categorical assignments show agreement with the expected physical phases in the Ising model, the ferromagnetic spin-up and spin down phases as well as the high temperature weak external field phase. Additionally, configurations associated with the crossover phenomena are predicted by the model. The classification probabilities allow for a robust method of estimating the critical temperature in the vanishing field case, showing exceptional agreement with the known physics. This work indicates that a representation learning approach using an adversarial neural network can be used to identify categories that strongly resemble physical phases with no a priori information beyond raw physical configurations and the physical conditions they are subject to. Proper implementation of finite size scaling is essential for a complete machine learning approach in order to bring it in line with established statistical mechanics, which is worthwhile for future study.
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