论文标题
减少统计物理模拟的临界减速
Reduced critical slowing down for statistical physics simulations
论文作者
论文摘要
Wang-Landau模拟提供了在集体坐标和随机配置空间上明确集成的可能性。我们建议选择所谓的“慢模式”，该模式负责大型自相关时间，从而降低了关键的速度，以进行集体集成。我们将ISING模型的建议和线性删除方法（LLR）方法作为仿真算法。首先，我们在一个自发断裂的对称性和热浴算法的阶段中证明了超级关键的放慢速度，为此，自相关时间随着系统大小而呈指数增长。相比之下，使用磁化作为集体坐标，我们提供了证据表明，超级关键的放缓是不存在的。我们仍然观察到自相关时间的多项式增加，体积（临界减速），但是与局部更新技术相比，这是通过数量级降低的。
Wang-Landau simulations offer the possibility to integrate explicitly over a collective coordinate and stochastically over the remainder of configuration space. We propose to choose the so-called "slow mode", which is responsible for large autocorrelation times and thus critical slowing down, for collective integration. We study this proposal for the Ising model and the linear-log-relaxation (LLR) method as simulation algorithm. We firstly demonstrate super critical slowing down in a phase with spontaneously broken symmetry and for the heatbath algorithms, for which autocorrelation times grow exponentially with system size. By contrast, using the magnetisation as collective coordinate, we present evidence that super critical slowing down is absent. We still observe a polynomial increase of the autocorrelation time with volume (critical slowing down), which is however reduced by orders of magnitude when compared to local update techniques.
