论文标题
避免使用SALR互动的模型中的严重放缓
Avoiding critical slowdown in models with SALR interactions
论文作者
论文摘要
在有挫败感的系统中，动力学的关键放缓严重阻碍了相关模型的数值研究。为了帮助避开类似凝胶的迟缓，需要对基本物理学有更清晰的了解。在这里，我们首先通过研究示意性挫折模型的一维和伯特晶格版本，即轴向的下一个最新的邻居Ising（Annni）模型，从而获得对该现象的通用见解。基于这些发现，我们制定了两种群集算法，这些算法加快了2D平方晶格上Annni模型的模拟。尽管这些方案并不能避免关键的放缓，但在某些制度中，可以实现多达40个因素的加速。
In systems with frustration, the critical slowdown of the dynamics severely impedes the numerical study of phase transitions for even the simplest of lattice models. In order to help sidestep the gelation-like sluggishness, a clearer understanding of the underlying physics is needed. Here, we first obtain generic insights into that phenomenon by studying one-dimensional and Bethe lattice versions of a schematic frustrated model, the axial next-nearest neighbor Ising (ANNNI) model. Based on these findings, we formulate two cluster algorithms that speed up the simulations of the ANNNI model on a 2D square lattice. Although these schemes do not avoid the critical slowdown, speed-ups of factors up to 40 are achieved in some regimes.
