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

Wang, Pascal; Castellana, Daniele; Dijkstra, Henk A.

doi:https://doi.org/10.5194/npg-28-135-2021

Articles | Volume 28, issue 1

https://doi.org/10.5194/npg-28-135-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-28-135-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 1

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

Data sets

Dataset for “Improvements to the use of the Trajectory-Adaptive Multilevel Sampling algorithm for the study of rare events” P. Wang and D. Castellana https://doi.org/10.6084/m9.figshare.13918100.v1

Model code and software

Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements P. Wang https://doi.org/10.6084/m9.figshare.13914353.v2

Python implementation of the geometric minimum action method (gMAM) P. Wang https://doi.org/10.6084/m9.figshare.13919642.v1

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.