Machine learning etudes in conformal field theories
Machine learning etudes in conformal field theories
复制标题
共形场论中的机器学习研究
DOI：
10.1142/s2810939222500058
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
M. Zaz
中科院分区：
文献类型：
--
作者：
Heng;Yang;Shailesh Lal;M. Zaz
我们证明了共形场论的各个方面都适用于机器学习。相对适度的前馈神经网络能够区分三点函数的尺度和共形不变性，并以接近100%的准确度识别交叉对称的四点函数。此外，神经网络还能够识别出现在假定的CFT四点函数中的共形块，并预测相应的算子乘积展开（OPE）系数的值。神经网络还成功地通过检查OPE数据，在CFT中的离散对称性下通过量子数对初级算子进行分类。我们还证明了神经网络能够学习3D伊辛模型中标量相关函数的可用OPE数据，并通过回归预测标量OPE通道中出现的高自旋算子的扭曲。
We demonstrate that various aspects of Conformal Field Theory are amenable to machine learning. Relatively modest feed-forward neural networks are able to distinguish between scale and conformal invariance of a three-point function and identify a crossing-symmetric four-point function to nearly 100% accuracy. Furthermore, neural networks are also able to identify conformal blocks appearing in a putative CFT four-point function and predict the values of the corresponding operator product expansions (OPE) coefficients. Neural networks also successfully classify primary operators by their quantum numbers under discrete symmetries in the CFT from examining OPE data. We also demonstrate that neural networks are able to learn the available OPE data for scalar correlation function in the 3D Ising model and predict the twists of higher-spin operators that appear in scalar OPE channels by regression.
影响因子：
5.4
作者：
Carifio, Jonathan;Halverson, James;Nelson, Brent D.
通讯作者：
Nelson, Brent D.
机器学习遇上数论：Birch-Swinnerton-Dyer 的数据科学
DOI：
--
发表时间：
2019
期刊：
ArXiv
影响因子：
--
作者：
Laura Alessandretti;Andrea Baronchelli;Yang
通讯作者：
Yang
影响因子：
5
作者：
Piscopo, Maria Laura;Spannowsky, Michael;Waite, Philip
通讯作者：
Waite, Philip