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Unpublished Paper
Multilevel Coarse Graining and Nano-Pattern Discovery in Many Particle Stochastic Systems
Journal of Comp (2012)
  • Evangelia Kalligiannski
  • Markos Katsoulakis, University of Massachusetts - Amherst
  • Petr Plechac
  • Dion Vlachos
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.
  • Markov chain Monte Carlo,
  • coarse graining,
  • lattice systems,
  • phase transitions,
  • pattern formation
Publication Date
Prepublished version downloaded from ArXiv. Published version is located at
Citation Information
Evangelia Kalligiannski, Markos Katsoulakis, Petr Plechac and Dion Vlachos. "Multilevel Coarse Graining and Nano-Pattern Discovery in Many Particle Stochastic Systems" Journal of Comp (2012)
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