Abstract. The Trajectory-Adaptive Multilevel Sampling (TAMS) is a promising method to determine probabilities of noise-induced transition in multi-stable high-dimensional dynamical systems. In this paper, we focus on two improvements of the current algorithm related to (i) the choice of the target set and (ii) the formulation of the score function. In particular, we use confidence ellipsoids determined from linearised dynamics in the choice of the target set. Furthermore, we define a score function based on empirical transition paths computed at relatively high noise levels. The suggested new TAMS method is applied to two typical problems illustrating the benefits of the modifications.

摘要。轨迹自适应多级采样（TAMS）是一种有前途的方法，可以确定多稳态高维动力系统中噪声引起的跃迁概率。在本文中，我们重点关注当前算法的两个改进，这些改进与（i）目标集的选择和（ii）得分函数的制定有关。特别是，我们在选择目标集时使用了根据线性化动力学确定的置信椭圆体。此外，我们基于在相对较高的噪声水平下计算出的经验转换路径定义得分函数。建议的新TAMS方法应用于两个典型问题，这些问题说明了修改的好处。