对比生成与判别方法在伊辛相变检测中的表现
Machine learning the Ising transition: A comparison between discriminative and generative approaches
- 用判别与生成模型分别处理伊辛模型相变分类
- 生成模型在小数据下表现更优，判别模型在大数据下更准
- 为物理相变检测提供模型选择依据，适合机器学习入门者
相变检测是多体物理的核心任务。为实现自动化，该问题可转化为分类任务。分类方法可分为判别式与生成式两类，但二者适用性尚不明确，取决于系统知识、数据规模、精度需求和计算资源等因素。本文通过数值实验，研究经典二维正方晶格铁磁伊辛模型热相变中的分类问题，比较判别与生成方法的性能表现，旨在回答如何针对相变分类任务选择合适方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。