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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/npg-2020-35
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2020-35
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Aug 2020

27 Aug 2020

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This preprint is currently under review for the journal NPG.

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

Pascal Wang1, Daniele Castellana2, and Henk Dijkstra2,3 Pascal Wang et al.
  • 1Univ. Lyon, ENS de Lyon, Univ. Claude Bernard, CNRS, Laboratoire de Physique, Lyon, France
  • 2Institute for Marine and Atmospheric research Utrecht, Department of Physics, Utrecht University, Utrecht, the Netherlands
  • 3Centre for Complex Systems Studies, Department of Physics, Utrecht University, Utrecht, the Netherlands

Abstract. The Trajectory-Adaptive Multilevel Sampling (TAMS) is a promising method to determine probabilities of noise induced transition in multi-stable high-dimensional dynamical systems. In this paper, we focus on two improvements of the current algorithm related to (i) the choice of the target set and (ii) the formulation of the score function. In particular, we use confidence ellipsoids determined from linearized dynamics in the choice of the target set. Furthermore, we define a score function based on empirical transition paths computed at relatively high noise levels. The suggested new TAMS method is applied to two typical problems illustrating the benefits of the modifications.

Pascal Wang et al.

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Pascal Wang et al.

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Short summary
This paper proposes two improvements to the use of the Trajectory-Adaptive Multilevel Sampling, a rare-event algorithm which computes noise-induced transition probabilities. The first improvement uses locally linearized 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.
This paper proposes two improvements to the use of the Trajectory-Adaptive Multilevel Sampling,...
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