Multiscale Reaction-Diffusion Algorithms: PDE-Assisted Brownian Dynamics

Handle URI:
http://hdl.handle.net/10754/598917
Title:
Multiscale Reaction-Diffusion Algorithms: PDE-Assisted Brownian Dynamics
Authors:
Franz, Benjamin; Flegg, Mark B.; Chapman, S. Jonathan; Erban, Radek
Abstract:
Two algorithms that combine Brownian dynami cs (BD) simulations with mean-field partial differential equations (PDEs) are presented. This PDE-assisted Brownian dynamics (PBD) methodology provides exact particle tracking data in parts of the domain, whilst making use of a mean-field reaction-diffusion PDE description elsewhere. The first PBD algorithm couples BD simulations with PDEs by randomly creating new particles close to the interface, which partitions the domain, and by reincorporating particles into the continuum PDE-description when they cross the interface. The second PBD algorithm introduces an overlap region, where both descriptions exist in parallel. It is shown that the overlap region is required to accurately compute variances using PBD simulations. Advantages of both PBD approaches are discussed and illustrative numerical examples are presented. © 2013 Society for Industrial and Applied Mathematics.
Citation:
Franz B, Flegg MB, Chapman SJ, Erban R (2013) Multiscale Reaction-Diffusion Algorithms: PDE-Assisted Brownian Dynamics. SIAM Journal on Applied Mathematics 73: 1224–1247. Available: http://dx.doi.org/10.1137/120882469.
Publisher:
Society for Industrial & Applied Mathematics (SIAM)
Journal:
SIAM Journal on Applied Mathematics
KAUST Grant Number:
KUK-C1-013-04
Issue Date:
19-Jun-2013
DOI:
10.1137/120882469
Type:
Article
ISSN:
0036-1399; 1095-712X
Sponsors:
Received by the editors June 26, 2012; accepted for publication (in revised form) March 6, 2013; published electronically June 19, 2013. The research leading to these results has received funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 239870. This publication was based on work supported in part by Award KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST).Corresponding author. Mathematical Institute, University of Oxford, Oxford OX1 3LB, UK (erban@maths.ox.ac.uk). This author's work was supported by a Royal Society University Research Fellowship; by a Nicholas Kurti Junior Fellowship of Brasenose College, University of Oxford; and by a Philip Leverhulme Prize awarded by the Leverhulme Trust.
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Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorFranz, Benjaminen
dc.contributor.authorFlegg, Mark B.en
dc.contributor.authorChapman, S. Jonathanen
dc.contributor.authorErban, Radeken
dc.date.accessioned2016-02-25T13:43:41Zen
dc.date.available2016-02-25T13:43:41Zen
dc.date.issued2013-06-19en
dc.identifier.citationFranz B, Flegg MB, Chapman SJ, Erban R (2013) Multiscale Reaction-Diffusion Algorithms: PDE-Assisted Brownian Dynamics. SIAM Journal on Applied Mathematics 73: 1224–1247. Available: http://dx.doi.org/10.1137/120882469.en
dc.identifier.issn0036-1399en
dc.identifier.issn1095-712Xen
dc.identifier.doi10.1137/120882469en
dc.identifier.urihttp://hdl.handle.net/10754/598917en
dc.description.abstractTwo algorithms that combine Brownian dynami cs (BD) simulations with mean-field partial differential equations (PDEs) are presented. This PDE-assisted Brownian dynamics (PBD) methodology provides exact particle tracking data in parts of the domain, whilst making use of a mean-field reaction-diffusion PDE description elsewhere. The first PBD algorithm couples BD simulations with PDEs by randomly creating new particles close to the interface, which partitions the domain, and by reincorporating particles into the continuum PDE-description when they cross the interface. The second PBD algorithm introduces an overlap region, where both descriptions exist in parallel. It is shown that the overlap region is required to accurately compute variances using PBD simulations. Advantages of both PBD approaches are discussed and illustrative numerical examples are presented. © 2013 Society for Industrial and Applied Mathematics.en
dc.description.sponsorshipReceived by the editors June 26, 2012; accepted for publication (in revised form) March 6, 2013; published electronically June 19, 2013. The research leading to these results has received funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 239870. This publication was based on work supported in part by Award KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST).Corresponding author. Mathematical Institute, University of Oxford, Oxford OX1 3LB, UK (erban@maths.ox.ac.uk). This author's work was supported by a Royal Society University Research Fellowship; by a Nicholas Kurti Junior Fellowship of Brasenose College, University of Oxford; and by a Philip Leverhulme Prize awarded by the Leverhulme Trust.en
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)en
dc.subjectBrownian dynamicsen
dc.subjectMultiscale simulationen
dc.subjectReaction-diffusion systemsen
dc.titleMultiscale Reaction-Diffusion Algorithms: PDE-Assisted Brownian Dynamicsen
dc.typeArticleen
dc.identifier.journalSIAM Journal on Applied Mathematicsen
dc.contributor.institutionUniversity of Oxford, Oxford, United Kingdomen
kaust.grant.numberKUK-C1-013-04en
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