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arXiv 1801.05784hep-latcond-mat.dis-nn
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格子量子色动力学中的机器学习action参数

Machine learning action parameters in lattice quantum chromodynamics

Phiala E. Shanahan, Amalie Trewartha, William Detmold

摘要:

强相互作用的数值格子量子色动力学研究在粒子和核物理的许多方面都是重要的。这样的研究需要大量的计算资源来进行。许多提出的方法承诺提高效率的晶格计算,并访问目前计算上难以处理的参数空间的区域,通过多尺度动作匹配的方法,需要生成的晶格数据集的参数回归。研究了机器学习对这种回归任务的适用性,发现深度神经网络即使在主成分分析等方法失败的情况下也能提供有效的解决方案。格点量子色动力学数据集固有的高信息含量和复杂的对称性要求引入自定义神经网络层,并为进一步发展提供了机会。

英文摘要:

Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. The high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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