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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Cited articles

Ashwin, P., Wieczorek, S., Vitolo, R., and Cox, P.: Tipping points in open systems: bifurcation, noise-induced and rate-dependent examples in the climate system, Philos. Trans. Royal Soc. A, 370, 1166–1184, https://doi.org/10.1098/rsta.2011.0306, 2012. a
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Baars, S., Castellana, D., Wubs, F. W., and Dijkstra, H. A.: Application of adaptive multilevel splitting to high-dimensional dynamical systems, J. Comput. Phys., 424, 109876, https://doi.org/10.1016/j.jcp.2020.109876, 2021. a
Ben-Israel, A. and Greville, T. N.: Generalized inverses: theory and applications, vol. 15, Springer Science & Business Media, Springer-Verlag New York, 2003. a
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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.