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arXiv 1202.0316cond-mat.stat-mech
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转向过渡路径采样

Steered Transition Path Sampling

Nicholas Guttenberg, Aaron R. Dinner, Jonathan Weare

摘要:

本文介绍了一种获得任意随机动力系统统计特性的路径抽样方法。该方法通过在时间上分解轨迹,估计满足进度约束的概率,基于该概率修改动态,然后重新加权以计算平均值来工作。因为进度约束可以根据时间间隔内事件的发生来公式化,所以该方法特别适合于控制动态事件的电流的采样。我们证明了计算跃迁概率的方法在障碍交叉问题和生存概率的强扩散系统的吸收状态,这是很难处理的拍摄。我们讨论了该算法与其他方法的关系。

英文摘要:

We introduce a path sampling method for obtaining statistical properties of an arbitrary stochastic dynamics. The method works by decomposing a trajectory in time, estimating the probability of satisfying a progress constraint, modifying the dynamics based on that probability, and then reweighting to calculate averages. Because the progress constraint can be formulated in terms of occurrences of events within time intervals, the method is particularly well suited for controlling the sampling of currents of dynamic events. We demonstrate the method for calculating transition probabilities in barrier crossing problems and survival probabilities in strongly diffusive systems with absorbing states, which are difficult to treat by shooting. We discuss the relation of the algorithm to other methods.

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。

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