Work

Improved Diffusion Monte Carlo

Year: 2014

Type: article

Abstract: We propose a modification, based on the RESTART (repetitive simulation trials after reaching thresholds) and DPR (dynamics probability redistribution) rare event simulation algorithms, of the standard diffusion Monte Carlo (DMC) algorithm. The new algorithm has a lower variance per workload, regardless of the regime considered. In particular, it makes it feasible to use DMC in situations where the “naïve” generalization of the standard algorithm would be impractical due to an exponential explosion of its variance. We numerically demonstrate the effectiveness of the new algorithm on a standard rare event simulation problem (probability of an unlikely transition in a Lennard‐Jones cluster), as well as a high‐frequency data assimilation problem. © 2014 Wiley Periodicals, Inc.

Source: Communications on Pure and Applied Mathematics

DOI: 10.1002/cpa.21526

Authors: Martin Hairer, Jonathan Weare

Institutions: University of Warwick, University of Chicago

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Leverhulme Trust, Royal Society, Engineering and Physical Sciences Research Council

Awards: G3175175803, G6790778079