用加权平均法模拟自旋晶格模型的热边界条件
Simulating thermal boundary conditions of spin-lattice models with weighted averages
摘要：
热边界条件在揭示短程自旋玻璃的性质方面起着越来越重要的作用，并且可能也与其他无序系统有关。扩散方法初始化每个副本与一个随机的边界条件，在无限的温度下，使用人口退火已被用于最近的大规模模拟。然而，这种方法的效率可以大大抑制，因为温度混沌。例如，大多数样本都有一些边界条件，这些条件在低温退火过程中从总体中完全消除。在这项工作中，我研究了一种加权平均法来解决这个问题，分别模拟每个边界条件和收集数据使用加权平均。采用粒子群退火和并行回火两种方法对这两种方法的效率进行了研究，结果表明加权平均方法更有效、更准确。
英文摘要：
Thermal boundary conditions has played an increasingly important role in revealing the nature of short-range spin glasses and is likely to be relevant also for other disordered systems. Diffusion method initializing each replica with a random boundary condition at the infinite temperature using population annealing has been used in recent large-scale simulations. However, the efficiency of this method can be greatly suppressed because of temperature chaos. For example, most samples have some boundary conditions that are completely eliminated from the population in the process of annealing at low temperatures. In this work, I study a weighted average method to solve this problem by simulating each boundary conditions separately and collect data using weighted averages. The efficiency of the two methods are studied using both population annealing and parallel tempering, showing that the weighted average method is more efficient and accurate.
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