Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

Importance sampling of unbounded random stopping times: computing committor functions and exit rates without reweighting

under review
  • Rare events in molecular dynamics are often related to noise-induced transitions between different macroscopic states (e.g., in protein folding). A common feature of these rare transitions is that they happen on timescales that are on average exponentially long compared to the characteristic timescale of the system, with waiting time distributions that have (sub)exponential tails and infinite support. As a result, sampling such rare events can lead to trajectories that can be become arbitrarily long, with not too low probability, which makes the reweighting of such trajectories a real challenge. Here, we discuss rare event simulation by importance sampling from a variational perspective, with a focus on applications in molecular dynamics, in particular the computation of committor functions. The idea is to design importance sampling schemes that (a) reduce the variance of a rare event estimator while controlling the average length of the trajectories and (b) that do not require the reweighting of possibly very long trajectories. In doing so, we study different stochastic control formulations for committor and mean first exit times, which we compare both from a theoretical and a computational point of view, including numerical studies of some benchmark examples.

Export metadata

Additional Services

Search Google Scholar Statistics - number of accesses to the document
Metadaten
Author:Carsten Hartmann, Annika Jöster, Christof Schütte, Alexander Sikorski, Marcus Weber
Document Type:Article
Year of first publication:2026
ArXiv Id:http://arxiv.org/abs/2601.01489
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.