Handle URI:
http://hdl.handle.net/10754/598851
Title:
Model reduction using a posteriori analysis
Authors:
Whiteley, Jonathan P.
Abstract:
Mathematical models in biology and physiology are often represented by large systems of non-linear ordinary differential equations. In many cases, an observed behaviour may be written as a linear functional of the solution of this system of equations. A technique is presented in this study for automatically identifying key terms in the system of equations that are responsible for a given linear functional of the solution. This technique is underpinned by ideas drawn from a posteriori error analysis. This concept has been used in finite element analysis to identify regions of the computational domain and components of the solution where a fine computational mesh should be used to ensure accuracy of the numerical solution. We use this concept to identify regions of the computational domain and components of the solution where accurate representation of the mathematical model is required for accuracy of the functional of interest. The technique presented is demonstrated by application to a model problem, and then to automatically deduce known results from a cell-level cardiac electrophysiology model. © 2010 Elsevier Inc.
Citation:
Whiteley JP (2010) Model reduction using a posteriori analysis. Mathematical Biosciences 225: 44–52. Available: http://dx.doi.org/10.1016/j.mbs.2010.01.008.
Publisher:
Elsevier BV
Journal:
Mathematical Biosciences
KAUST Grant Number:
KUK-C1-013-04
Issue Date:
May-2010
DOI:
10.1016/j.mbs.2010.01.008
PubMed ID:
20117117
Type:
Article
ISSN:
0025-5564
Sponsors:
This publication is based on work supported by Award No. KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorWhiteley, Jonathan P.en
dc.date.accessioned2016-02-25T13:42:26Zen
dc.date.available2016-02-25T13:42:26Zen
dc.date.issued2010-05en
dc.identifier.citationWhiteley JP (2010) Model reduction using a posteriori analysis. Mathematical Biosciences 225: 44–52. Available: http://dx.doi.org/10.1016/j.mbs.2010.01.008.en
dc.identifier.issn0025-5564en
dc.identifier.pmid20117117en
dc.identifier.doi10.1016/j.mbs.2010.01.008en
dc.identifier.urihttp://hdl.handle.net/10754/598851en
dc.description.abstractMathematical models in biology and physiology are often represented by large systems of non-linear ordinary differential equations. In many cases, an observed behaviour may be written as a linear functional of the solution of this system of equations. A technique is presented in this study for automatically identifying key terms in the system of equations that are responsible for a given linear functional of the solution. This technique is underpinned by ideas drawn from a posteriori error analysis. This concept has been used in finite element analysis to identify regions of the computational domain and components of the solution where a fine computational mesh should be used to ensure accuracy of the numerical solution. We use this concept to identify regions of the computational domain and components of the solution where accurate representation of the mathematical model is required for accuracy of the functional of interest. The technique presented is demonstrated by application to a model problem, and then to automatically deduce known results from a cell-level cardiac electrophysiology model. © 2010 Elsevier Inc.en
dc.description.sponsorshipThis publication is based on work supported by Award No. KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherElsevier BVen
dc.subjectA posteriori analysisen
dc.subjectModel reductionen
dc.subjectOrdinary differential equationen
dc.titleModel reduction using a posteriori analysisen
dc.typeArticleen
dc.identifier.journalMathematical Biosciencesen
dc.contributor.institutionUniversity of Oxford, Oxford, United Kingdomen
kaust.grant.numberKUK-C1-013-04en

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