Adaptive Stochastic Methods for Sampling Driven Systems

Andrew Jones, Benedict Leimkuhler

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Thermostatting methods are discussed in the context of canonical sampling in the presence of driving stochastic forces. Generalisations of the Nosé-Hoover method and Langevin dynamics are introduced which are able to dissipate excess heat introduced by steady Brownian perturbation (without a priori knowledge of its strength) while preserving ergodicity. Implementation and parameter selection are considered. It is demonstrated using numerical experiments that the methods derived can adaptively control the target canonical ensemble in the presence of nonlinear driving perturbations.
Original languageEnglish
Article number084125
Number of pages11
JournalThe Journal of Chemical Physics
Publication statusPublished - 2011

Keywords / Materials (for Non-textual outputs)

  • Brownian motion
  • stochastic differential equations
  • molecular simulation


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