摘要翻译：
我们发展了一种最近提出的重要性抽样蒙特卡罗算法，用于随机无序系统中稀有事件和熄灭变量的抽样。我们将它应用于一个二维键稀释Ising模型，研究了Griffiths奇点，该奇点被认为是由稀有大团簇的存在引起的。结果表明，反磁化率分布在原点处呈指数尾分布，这是Griffiths奇异性的结果。
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英文标题：
《A Monte Carlo Algorithm for Sampling Rare Events: Application to a
  Search for the Griffiths Singularity》
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作者：
Koji Hukushima, Yukito Iba
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最新提交年份：
2007
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分类信息：

一级分类：Physics        物理学
二级分类：Disordered Systems and Neural Networks        无序系统与神经网络
分类描述：Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜；随机、非周期和准周期系统的性质；无序介质中的传输；本地化；由缺陷和无序介导的现象；神经网络
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一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
  We develop a recently proposed importance-sampling Monte Carlo algorithm for sampling rare events and quenched variables in random disordered systems. We apply it to a two dimensional bond-diluted Ising model and study the Griffiths singularity which is considered to be due to the existence of rare large clusters. It is found that the distribution of the inverse susceptibility has an exponential tail down to the origin which is considered the consequence of the Griffiths singularity.
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PDF链接：
https://arxiv.org/pdf/711.087