Multilevel coarse graining and nano-pattern discovery in many particle stochastic systems
type: Item
instance of: scholarly article
title: Multilevel coarse graining and nano-pattern discovery in many particle stochastic systems
zbMATH Open document ID: 1247.82009
author: Evangelia Kalligiannaki
author: Markos Katsoulakis
author: Petr Plecháč
author: Dionisios G. Vlachos
published in: Journal of Computational Physics
publication date: 2012-05-18T00:00:00Z
full work available at URL: https://arxiv.org/abs/1109.0459
review text: The authors consider accelerating microscopic simulation methods with competing short and long range interactions arising in numerous physical systems and propose the multilevel coarse graining Monte Carlo method based on a hybrid statistical mechanics and statistics approach. 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 the microscopic level. Applications in phase transition and pattern formation problems confirm the efficiency of the proposed methods.
reviewed by: Nasir N. Ganikhodjaev
Mathematics Subject Classification ID: 82B20
Mathematics Subject Classification ID: 82B26
Mathematics Subject Classification ID: 82B80
Mathematics Subject Classification ID: 65C40
Mathematics Subject Classification ID: 65C05
zbMATH DE Number: 6036616
zbMATH Keywords: Markov chain Monte Carlo
zbMATH Keywords: coarse graining
zbMATH Keywords: lattice systems
zbMATH Keywords: phase transitions
zbMATH Keywords: pattern formation
Wikidata QID: Q60149113
MaRDI profile type: Publication
OpenAlex ID: W2020159488
arXiv ID: 1109.0459
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DOI: 10.1016/J.JCP.2011.12.011
description: scientific article; zbMATH DE number 6036616