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Optimal Parameter Upscaling for Partial Differential Equation Models in Mathematical Biology


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dc.contributorChinedu Eleh, nedueleh@gmail.comen_US
dc.creatorEleh, Chinedu
dc.creatorvan Wyk, Hans-Werner
dc.date.accessioned2024-04-04T13:02:45Z
dc.date.available2024-04-04T13:02:45Z
dc.date.created2022-04-06
dc.identifier.urihttp://webhome.auburn.edu/~cae0027/resources/notes/jmm-22.pdfen_US
dc.identifier.urihttps://meetings.ams.org/math/jmm2022/meetingapp.cgi/Session/3522en_US
dc.identifier.urihttps://aurora.auburn.edu/handle/11200/50635
dc.identifier.urihttp://dx.doi.org/10.35099/aurora-703
dc.description.abstractPartial differential equation models in mathematical biology often involve space-dependent parameters, such as diffusion coefficients and advection fields, that cannot be measured explicitly and are therefore uncertain. In this work, we compute spatially adaptive, lower-dimensional approximations of these fields, using machine learning tools. Such parsimonious representations of the parameter space would greatly improve the efficiency of the resulting stochastic simulations, allow for more targeted use of reduced order models, and aid in the related design of interventions. Numerical examples demonstrate our theoretical results.en_US
dc.formatPDFen_US
dc.publisherAmerican Mathematical Societyen_US
dc.relation.ispartofAMS Special Session on Mathematical Modeling of Biological Processesen_US
dc.rightsCC BY 4.0en_US
dc.subjectUpscaling, Multi-Scale Modeling, Polynomial Chaos Expansionen_US
dc.titleOptimal Parameter Upscaling for Partial Differential Equation Models in Mathematical Biologyen_US
dc.typeTexten_US
dc.type.genrePresentation, Paper Presentationen_US
dc.citation.volumeSS126Aen_US
dc.locationVirtualen_US
dc.creator.orcid0000-0002-4531-5263en_US

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