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dc.contributor.authorBen Hammouda, Chiheb
dc.contributor.authorMoraes, Alvaro
dc.contributor.authorTempone, Raul
dc.date.accessioned2017-06-08T06:32:26Z
dc.date.available2017-06-08T06:32:26Z
dc.date.issued2016-01-06
dc.identifier.urihttp://hdl.handle.net/10754/624790
dc.description.abstractThe dynamics of biochemical reactive systems with small copy numbers of one or more reactant molecules is dominated by stochastic effects. For those systems, discrete state-space and stochastic simulation approaches were proved to be more relevant than continuous state-space and deterministic ones. In systems characterized by having simultaneously fast and slowtimescales, the existing discrete space-state stochastic path simulation methods such as the stochastic simulation algorithm (SSA) and the explicit tauleap method can be very slow. Implicit approximations were developed in the literature to improve numerical stability and provide efficient simulation algorithms for those systems. In this work, we propose an efficient Multilevel Monte Carlo method in the spirit of the work by Anderson and Higham (2012) that uses drift-implicit tau-leap approximations at levels where the explicit tauleap method is not applicable due to numerical stability issues. We present numerical examples that illustrate the performance of the proposed method.
dc.subjectSampling
dc.titleMultilevel Drift-Implicit Tau-Leap
dc.typePoster
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
kaust.personBen Hammouda, Chiheb
kaust.personMoraes, Alvaro
kaust.personTempone, Raul
refterms.dateFOA2018-06-14T04:16:43Z


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