A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification

作者：Wu, Keyi; Li, Jinglai^*

来源：Journal of Computational Physics, 2016, 321: 1098-1109.

DOI：10.1016/j.jcp.2016.06.020

• Gaussian processes
• Multicanonical Monte Carlo
• Uncertainty quantification

In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parametery 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 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.

• 出版日期2016-9-15
• 单位上海交通大学