在Domany-Kinzel模型的监督,半监督和无监督学习
Kui Tuo1, Wei Li1, Shengfeng Deng2
1Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, <a href="https://ror.org/03x1jna21">Central China Normal University</a>, Wuhan 430079, China.
Physical review. E
|September 19, 2024
概括
机器学习方法在Domany-Kinzel (DK) 模型中揭示了关键点和指数.
科学领域:
- 复杂的系统复杂的系统.
- 统计物理学的统计物理.
- 机器学习是机器学习.
背景情况:
- 多马尼-金泽尔 (DK) 模型表现出多种不平衡阶段过渡.
- 了解这些模型中的关键行为对于统计物理学至关重要.
研究的目的:
- 应用机器学习技术来分析 (1+1) 维的DK模型中的相位过渡.
- 用各种学习方法估计关键点和相关指数.
主要方法:
- 使用了监督,半监督和无监督的学习方法.
- 主要组件分析 (PCA) 和自动编码器被用于关键点预测.
主要成果:
- 监督和半监督学习准确估计了关键点和相关指数.
- PCA和自动编码器与模拟的粒子数密度对临界点预测有很好的一致性.
结论:
- 机器学习为研究DK模型等复杂模型中的相位过渡提供了强大的工具.
- 这些方法提供了关键现象的准确估计,即使使用未标记的数据.
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