机器学习领域的适应在旋转模型中,具有连续的相位过渡
Vladislav Chertenkov1, Lev Shchur1
1HSE University, Landau Institute for Theoretical Physics, 142432 Chernogolovka, Russia and Laboratory for Computational Physics, 101000 Moscow, Russia.
Physical review. E
|October 21, 2025
概括
这项研究探讨了神经网络转移学习对于物理学中的关键现象. 虽然临界温度估计是准确的,但临界长度指数预测显示出变化,特别是在跨模型测试中.
科学领域:
- 统计力学 统计力学
- 机器学习应用 机器学习应用
- 计算物理 计算物理
背景情况:
- 了解普遍性类是统计力学中的关键.
- 神经网络为分析复杂的物理系统提供了新的方法.
- 物理学的转移学习旨在利用不同系统的训练模型.
研究的目的:
- 为了研究在旋转网格模型上训练的神经网络的可转移性.
- 评估网络性能,预测不同普遍性类的关键性质 (关键温度,相关长度指数).
- 确定神经网络模型具有普遍性的条件.
主要方法:
- 监督学习应用于三种二维模型:伊辛,四态波茨和巴克斯特-伍.
- 使用了自旋配置和结合能配置的数据集.
- 进行了直接的培训/测试和模型之间的交叉测试.
主要成果:
- 临界温度估计显示与直接训练的已知值保持良好一致.
- 使用能源数据集的四态波茨和伊辛模型之间的临界温度估计的交叉测试不那么准确.
- 临界长度指数估计不那么一致,能源数据集在一些交叉测试场景 (例如,Ising和Baxter-Wu模型) 中产生了更准确的结果.
结论:
- 神经网络在估计关键温度方面表现有前途,但在转移学习方面需要仔细考虑.
- 在不同的普遍性类中预测相关长度指数仍然具有挑战性.
- 数据集的选择 (旋转与能量) 在关键现象分析中显著影响转移学习表现.
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