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dc.contributor.authorMoraes, Alvaro
dc.contributor.authorTempone, Raul
dc.contributor.authorVilanova, Pedro
dc.date.accessioned2017-06-05T08:35:48Z
dc.date.available2017-06-05T08:35:48Z
dc.date.issued2015-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624086
dc.description.abstractIn this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks specifically designed for systems in which the set of reaction channels can be adaptively partitioned into two subsets characterized by either “high” or “low” activity. To estimate expected values of observables of the system, our method bounds the global computational error to be below a prescribed tolerance, within a given confidence level. This is achieved with a computational complexity of order O (TOL-2).We also present a novel control variate technique which may dramatically reduce the variance of the coarsest level at a negligible computational cost. Our numerical examples show substantial gains with respect to the standard Stochastic Simulation Algorithm (SSA) by Gillespie and also our previous hybrid Chernoff tau-leap method.
dc.titleA multilevel adaptive reaction-splitting method for SRNs
dc.typePoster
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateJanuary 6-9, 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST
kaust.personMoraes, Alvaro
kaust.personTempone, Raul
kaust.personVilanova, Pedro
refterms.dateFOA2018-06-14T03:18:59Z


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