arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.01682cond-mat.stat-mech
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二维正方形Ising算子的InfoCGAN分类

InfoCGAN Classification of 2-Dimensional Square Ising Configurations

Nicholas Walker, Ka Ming Tam

摘要:

一个InfoCGAN神经网络的训练2维平方伊辛配置条件下的外部施加的磁场和温度。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,而神经网络学习通过辅助网络提供的附加分类分配预测来区分“真实”和“假”配置。一些预测的分类分配与伊辛模型中预期的物理相一致,铁磁自旋上和自旋下阶段以及高温弱外场阶段。此外,与交叉现象相关联的配置预测的模型。的分类概率允许一个强大的方法来估计的临界温度在消失场的情况下,显示与已知的物理异常协议。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。

英文摘要:

An InfoCGAN neural network is trained on 2-dimensional 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.

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。

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