论文标题
对二维ISING模型的深入学习，以用变异自动编码器提取交叉区域
Deep Learning on the 2-Dimensional Ising Model to Extract the Crossover Region with a Variational Autoencoder
论文作者
论文摘要
正方形晶格上的二维ISING模型在非变化场案例中使用变异自动编码器进行了研究，目的是在铁磁相和顺磁性相之间提取交叉区域。发现编码的潜在变量空间可提供合适的指标，用于跟踪ISING配置中的顺序和混乱，以与期望一致的方式延伸至交叉区域的提取。提取的结果实现了对临界点的特殊预测，并与先前发表的有关模型的配置磁化的结果达成共识。该方法的性能为使用机器学习从几乎没有先验数据的复杂物理系统中提取有意义的结构信息提供了鼓励。
The 2-dimensional Ising model on a square lattice is investigated with a variational autoencoder in the non-vanishing field case for the purpose of extracting the crossover region between the ferromagnetic and paramagnetic phases. The encoded latent variable space is found to provide suitable metrics for tracking the order and disorder in the Ising configurations that extends to the extraction of a crossover region in a way that is consistent with expectations. The extracted results achieve an exceptional prediction for the critical point as well as agreement with previously published results on the configurational magnetizations of the model. The performance of this method provides encouragement for the use of machine learning to extract meaningful structural information from complex physical systems where little a priori data is available.
