Articles | Volume 28, issue 1
https://doi.org/10.5194/npg-28-135-2021
https://doi.org/10.5194/npg-28-135-2021
Research article
 | 
24 Feb 2021
Research article |  | 24 Feb 2021

Improvements to the use of the Trajectory-Adaptive Multilevel Sampling algorithm for the study of rare events

Pascal Wang, Daniele Castellana, and Henk A. Dijkstra

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Latest update: 13 Dec 2024
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Short summary
This paper proposes two improvements to the use of Trajectory-Adaptive Multilevel Sampling, a rare-event algorithm which computes noise-induced transition probabilities. The first improvement uses locally linearised dynamics in order to reduce the arbitrariness associated with defining what constitutes a transition. The second improvement uses empirical transition paths accumulated at high noise in order to formulate the score function which determines the performance of the algorithm.