A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification-Reference-Cited by-同舟云学术

A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification

Published:2016-09 Issue: Volume:321 Page:1098-1109
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Wu Keyi,Li Jinglai^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modeling and Simulation,Numerical Analysis

Reference23 articles.

1. Applied Groundwater Modeling: Simulation of Flow and Advective Transport, vol. 4;Anderson,1992

2. A new adaptive importance sampling scheme for reliability calculations;Au;Struct. Saf.,1999

3. Introduction to multicanonical Monte Carlo simulations;Berg;Fields Inst. Commun.,2000

4. Markov Chain Monte Carlo Simulations and Their Statistical Analysis: With Web-Based Fortran Code;Berg,2004

5. Multicanonical algorithms for first order phase transitions;Berg;Phys. Lett. B,1991

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