A subset multicanonical Monte Carlo method for simulating rare failure events

Xinjuan Chen, Jinglai Li

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Estimating failure probabilities of engineering systems is an important problem in many engineering fields. In this work we consider such problems where the failure probability is extremely small (e.g. ). In this case, standard Monte Carlo methods are not feasible due to the extraordinarily large number of samples required. To address these problems, we propose an algorithm that combines the main ideas of two very powerful failure probability estimation approaches: the subset simulation (SS) and the multicanonical Monte Carlo (MMC) methods. Unlike the standard MMC which samples in the entire domain of the input parameter in each iteration, the proposed subset MMC algorithm adaptively performs MMC simulations in a subset of the state space, which improves the sampling efficiency. With numerical examples we demonstrate that the proposed method is significantly more efficient than both of the SS and the MMC methods. Moreover, like the standard MMC, the proposed algorithm can reconstruct the complete distribution function of the parameter of interest and thus can provide more information than just the failure probabilities of the systems.
Original languageEnglish
Pages (from-to)23-35
JournalJournal of Computational Physics
Early online date25 Apr 2017
Publication statusPublished - 1 Sept 2017


  • failure probability estimation
  • multicanonical Monte Carlo
  • subset simulation
  • uncertainty quantification


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