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dc.creatorFrank, M
dc.creatorSeibold, B
dc.date.accessioned2021-02-04T21:41:46Z
dc.date.available2021-02-04T21:41:46Z
dc.date.issued2011-09-01
dc.identifier.issn1937-5093
dc.identifier.issn1937-5077
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/6010
dc.identifier.other809KD (isidoc)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/6028
dc.description.abstractMoment methods are classical approaches that approximate the mesoscopic radiative transfer equation by a system of macroscopic moment equations. An expansion in the angular variables transforms the original equation into a system of infinitely many moments. The truncation of this infinite system is the moment closure problem. Many types of closures have been presented in the literature. In this note, we demonstrate that optimal prediction, an approach originally developed to approximate the mean solution of systems of nonlinear ordinary differential equations, can be used to derive moment closures. To that end, the formalism is generalized to systems of partial differential equations. Using Gaussian measures, existing linear closures can be re-derived, such as PN, diffusion, and diffusion correction closures. This provides a new perspective on several approximations done in the process and gives rise to ideas for modifications to existing closures. © American Institute of Mathematical Sciences.
dc.format.extent717-733
dc.language.isoen
dc.relation.haspartKinetic and Related Models
dc.relation.isreferencedbyAmerican Institute of Mathematical Sciences (AIMS)
dc.subjectRadiative transfer
dc.subjectmethod of moments
dc.subjectoptimal prediction
dc.subjectmeasure
dc.subjectdiffusion approximation
dc.titleOptimal prediction for radiative transfer: A new perspective on moment closure
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.3934/krm.2011.4.717
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-02-04T21:41:43Z
refterms.dateFOA2021-02-04T21:41:46Z


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