Importance Sampling of Rare Events in Chaotic Systems
arXiv:1701.06265 · doi:10.1140/epjb/e2017-80054-3
Abstract
Finding and sampling rare trajectories in dynamical systems is a difficult computational task underlying numerous problems and applications. In this paper we show how to construct Metropolis- Hastings Monte Carlo methods that can efficiently sample rare trajectories in the (extremely rough) phase space of chaotic systems. As examples of our general framework we compute the distribution of finite-time Lyapunov exponents (in different chaotic maps) and the distribution of escape times (in transient-chaos problems). Our methods sample exponentially rare states in polynomial number of samples (in both low- and high-dimensional systems). An open-source software that implements our algorithms and reproduces our results can be found in https://github.com/jorgecarleitao/chaospp
arXiv comment24 pages, 14 figures, Codes available in: https://github.com/jorgecarleitao/chaospp
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