标题： 机器学习伊辛过渡：判别方法和生成方法的比较 显示英文标题

标题： Machine learning the Ising transition: A comparison between discriminative and generative approaches

摘要： 相变的检测是多体物理学中的一个核心任务。为了实现这一过程的自动化，可以将其表述为一个分类问题。分类问题可以通过两种根本不同的方法来解决：判别法或生成法。一般来说，对于特定的问题，这两种方法中哪种更适合并不明确。这种选择预计会取决于系统知识的可用性、数据集大小、所需的精度、计算资源以及其他考虑因素。在这项工作中，我们通过在经典二维方形格子铁磁伊辛模型的热相变上进行数值案例研究，回答了如何解决相分类问题的方法问题。 显示英文摘要

摘要： The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems can be approached in two fundamentally distinct ways: through either a discriminative or a generative method. In general, it is unclear which of these two approaches is most suitable for a given problem. The choice is expected to depend on factors such as the availability of system knowledge, dataset size, desired accuracy, computational resources, and other considerations. In this work, we answer the question of how one should approach the solution of phase-classification problems by performing a numerical case study on the thermal phase transition in the classical two-dimensional square-lattice ferromagnetic Ising model.