Binning for efficient stochastic multiscale particle simulations

M. D. Michelotti, M. T. Heath, M. West

Research output: Contribution to journalArticlepeer-review

Abstract

Gillespie's Stochastic Simulation Algorithm (SSA) is an exact procedure for simulating the evolution of a collection of discrete, interacting entities, such as coalescing aerosol particles or reacting chemical species. The high computational cost of SSA has motivated the development of more efficient variants, such as Tau-Leaping, which sacrifices the exactness of SSA. For models whose interacting entities can be characterized by a continuous parameter, such as a measure of size for aerosol particles, we analyze strategies for accelerating these algorithms by aggregating particles of similar size into bins. We show that for such models an appropriate binning strategy can dramatically enhance efficiency, and in particular can make SSA computationally competitive without sacrificing exactness. These strategies are especially effective for highly multiscale problems. We formulate binned versions of both the SSA and Tau-Leaping algorithms and analyze and demonstrate their performance.

Original languageEnglish (US)
Pages (from-to)1071-1096
Number of pages26
JournalMultiscale Modeling and Simulation
Volume11
Issue number4
DOIs
StatePublished - 2013

Keywords

  • Atmospheric aerosol
  • Coagulation
  • Continuous-time Markov process
  • Monte carlo
  • Stochastic simulation algorithm

ASJC Scopus subject areas

  • Chemistry(all)
  • Modeling and Simulation
  • Ecological Modeling
  • Physics and Astronomy(all)
  • Computer Science Applications

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