An improved acceptance procedure for the hybrid Monte Carlo algorithm
Journal of Computational Physics
Monte Carlo algorithms for complex surface reaction mechanisms: efficiency and accuracy
Journal of Computational Physics
Algorithms for Image Processing and Computer Vision
Algorithms for Image Processing and Computer Vision
Coarse-grained stochastic processes and Monte Carlo simulations in lattice systems
Journal of Computational Physics
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models
SIAM Journal on Scientific Computing
Error Analysis of Coarse-Graining for Stochastic Lattice Dynamics
SIAM Journal on Numerical Analysis
Numerical and Statistical Methods for the Coarse-Graining of Many-Particle Stochastic Systems
Journal of Scientific Computing
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Multibody Interactions in Coarse-Graining Schemes for Extended Systems
SIAM Journal on Scientific Computing
Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms
Journal of Computational Physics
Hi-index | 31.45 |
In this work we propose a hierarchy of Markov chain Monte Carlo methods for sampling equilibrium properties of stochastic lattice systems with competing short and long range interactions. Each Monte Carlo step is composed by two or more sub-steps efficiently coupling coarse and finer state spaces. The method can be designed to sample the exact or controlled-error approximations of the target distribution, providing information on levels of different resolutions, as well as at the microscopic level. In both strategies the method achieves significant reduction of the computational cost compared to conventional Markov chain Monte Carlo methods. Applications in phase transition and pattern formation problems confirm the efficiency of the proposed methods.