A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification

Keyi Wu, Jinglai Li

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parameter y. The performance parameter y is random due to the presence of various sources of uncertainty in the system, and our goal is to estimate the probability density function (PDF) of y. We propose to use the multicanonical Monte Carlo (MMC) method, a special type of adaptive importance sampling algorithms, to compute the PDF of interest. Moreover, we develop an adaptive algorithm to construct local Gaussian process surrogates to further accelerate the MMC iterations. With numerical examples we demonstrate that the proposed method can achieve several orders of magnitudes of speedup over the standard Monte Carlo methods.
Original languageEnglish
Pages (from-to)1098-1109
JournalJournal of Computational Physics
Early online date17 Jun 2016
Publication statusPublished - 15 Sept 2016


  • Gaussian prosesses
  • multicanonical Monte Carlo
  • Uncertainty quantification


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