Steered Transition Path Sampling

Abstract

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.

11 pages, 8 figures

References in corpus (2)

• Non-Stationary Forward Flux Sampling
• Steered Transition Path Sampling

Cited by in corpus (11)

• Galerkin Approximation of Dynamical Quantities using Trajectory Data
• Pathways to self-organization: crystallization via nucleation and growth
• Practical rare event sampling for extreme mesoscale weather
• Reinforcement learning of rare diffusive dynamics
• Steered Transition Path Sampling
• Statistical behavior of adaptive multilevel splitting algorithms in simple models
• Rare Event Sampling Improves Mercury Instability Statistics
• Preserving Correlations Between Trajectories for Efficient Path Sampling
• Computing transition path theory quantities with trajectory stratification
• Variational deep learning of equilibrium transition path ensembles
• Influence of Inter-Layer Exchanges on Vorticity-Aligned Colloidal String Assembly in a Simple Shear Flow