张量模型的大N极限
A review of the large N limit of tensor models
摘要：
随机矩阵模型编码了随机二维表面的理论，并应用于弦理论，共形场论，随机几何中的统计物理和二维中的量子引力。他们成功的关键在于特胡夫特提出的1/N扩展。随机张量模型将随机矩阵推广到随机高维空间的理论。很长一段时间以来，张量的1/N展开都是未知的，它们的成功是有限的。最近的一系列结果改变了这一状况，实现了1/N展开到张量的推广。我们在本文中回顾这些结果。
英文摘要：
Random matrix models encode a theory of random two dimensional surfaces with applications to string theory, conformal field theory, statistical physics in random geometry and quantum gravity in two dimensions. The key to their success lies in the 1/N expansion introduced by 't Hooft. Random tensor models generalize random matrices to theories of random higher dimensional spaces. For a long time, no viable 1/N expansion for tensors was known and their success was limited. A series of recent results has changed this situation and the extension of the 1/N expansion to tensors has been achieved. We review these results in this paper.
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。
