使用机器学习的第一阶段过渡的相位概率
Diana Sukhoverkhova1, Vyacheslav Mozolenko1, Lev Shchur1
1HSE University, Landau Institute for Theoretical Physics, Chernogolovka 142432, Russia and , Moscow 101000, Russia.
深度机器学习研究了第一阶段的阶段过渡. 一个新的协议对旋转配置进行了分类,估计了波茨模型的相位概率和临界能量.
科学领域:
- 统计力学 统计力学
- 机器学习 机器学习
- 计算物理 计算物理
背景情况:
- 在具有第一阶段转换的系统中研究关键现象在计算上具有挑战性.
- 传统的方法很难准确地描述相位过渡附近的复杂行为.
研究的目的:
- 探索深度机器学习的应用,以分析第一阶段转换中的关键行为.
- 开发一种机器学习协议,用于分类系统阶段和估计过渡属性.
主要方法:
- 一个机器学习协议,使用即时旋转配置的三元分类.
- 在已知的无序和有序相能上训练神经网络.
- 使用训练有素的网络来预测给定能量的相位概率.
主要成果:
- 成功估计了Potts模型 (10和20组件) 的临界能量和潜在热量.
- 提供了第一个对属于有序,共存和无序相的配置概率的估计.
- 观察到相位概率可能表明共存阶段内的几何过渡.
结论:
- 深度机器学习为研究第一阶段过渡中的关键现象提供了一种强大的新方法.
- 拟议的协议准确地描述了相位行为,并量化了过渡参数.
- 这些发现为在相共存区域使用机器学习探索几何转换开辟了道路.
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