论文标题
采样限制了使用布朗桥的随机轨迹
Sampling constrained stochastic trajectories using Brownian bridges
论文作者
论文摘要
我们提出了一种基于布朗桥的兰格文动力学下进化的系统的调节轨迹的新方法。这些轨迹的条件是在太空中的一定点（或某个区域）结束。桥方方程可以精确地以非线性随机间距分化方程的形式重铸。当轨迹将轨迹紧密捆绑在一起时，即低温或过渡路径时，该方程可以很好地近似。近似方程可以使用固定点方法迭代求解。我们讨论了如何选择初始轨迹，并在某些简单问题上展示了该方法性能的一些示例。该方法允许以高精度生成条件轨迹。
We present a new method to sample conditioned trajectories of a system evolving under Langevin dynamics, based on Brownian bridges. The trajectories are conditioned to end at a certain point (or in a certain region) in space. The bridge equations can be recast exactly in the form of a non linear stochastic integro-differential equation. This equation can be very well approximated when the trajectories are closely bundled together in space, i.e. at low temperature, or for transition paths. The approximate equation can be solved iteratively, using a fixed point method. We discuss how to choose the initial trajectories and show some examples of the performance of this method on some simple problems. The method allows to generate conditioned trajectories with a high accuracy.
