Improving efficiency of the path optimization method for a gauge theory
Improving efficiency of the path optimization method for a gauge theory
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提高规范理论路径优化方法的效率
DOI：
10.1103/physrevd.107.034509
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发表时间：
2023
影响因子：
5
通讯作者：
Takase Hayato
中科院分区：
文献类型：
--
作者：
Namekawa Yusuke;Kashiwa Kouji;Matsuda Hidefumi;Ohnishi Akira;Takase Hayato
我们调查的效率规范协变神经网络和雅可比矩阵的近似优化复杂的集成路径，以避免在格场理论的符号问题。对于复杂集成路径的构建，我们采用了路径优化方法。用具有复规范耦合常数的二维U（1）规范理论作为实验室来评估效率。研究发现，由类Stout涂抹构成的规范协变神经网络可以像规范不变输入一样提高平均相位因子。对于雅可比矩阵的近似，我们测试了在学习过程中完全丢弃雅可比矩阵的最激烈的情况。它将雅可比矩阵计算的数值代价从减少到，即理论的自由度数目。使用这种雅可比近似的路径优化仍然以略微增加统计误差为代价来提高平均相位因子。
We investigate efficiency of a gauge-covariant neural network and an approximation of the Jacobian in optimizing the complexified integration path toward evading the sign problem in lattice field theories. For the construction of the complexified integration path, we employ the path optimization method. The two-dimensional U(1) gauge theory with the complex gauge-coupling constant is used as a laboratory to evaluate the efficiency. It is found that the gauge-covariant neural network, which is composed of the Stout-like smearing, can enhance the average phase factor, as the gauge-invariant input does. For the approximation of the Jacobian, we test the most drastic case in which we perfectly drop the Jacobian during the learning process. It reduces the numerical cost of the Jacobian calculation fromto, wheremeans the number of degrees of freedom of the theory. The path optimization using this Jacobian approximation still enhances the average phase factor at expense of a slight increase of the statistical error.
影响因子：
3.5
作者：
M. Fukuma;N. Matsumoto
通讯作者：
M. Fukuma;N. Matsumoto
世界体积混合蒙特卡罗算法的统计分析方法
影响因子：
3.5
作者：
Fukuma Masafumi;Matsumoto Nobuyuki;Namekawa Yusuke
通讯作者：
Namekawa Yusuke
晶格 θ-vacua 的数值模拟：以 2-d U(1) 规范理论作为测试用例
DOI：
10.1016/0550-3213(89)90051-5
发表时间：
1989
期刊：
Nuclear Physics
影响因子：
--
作者：
U. Wiese
通讯作者：
U. Wiese