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dc.contributor.authorSiripatana, Adil
dc.contributor.authorLe Maitre, Olivier
dc.contributor.authorKnio, Omar
dc.contributor.authorDawson, Clint
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2020-07-14T09:41:31Z
dc.date.available2020-07-14T09:41:31Z
dc.date.issued2020-07-01
dc.date.submitted2019-03-02
dc.identifier.citationSiripatana, A., Le Maitre, O., Knio, O., Dawson, C., & Hoteit, I. (2020). Bayesian inference of spatially varying Manning’s n coefficients in an idealized coastal ocean model using a generalized Karhunen-Loève expansion and polynomial chaos. Ocean Dynamics. doi:10.1007/s10236-020-01382-4
dc.identifier.issn1616-7228
dc.identifier.issn1616-7341
dc.identifier.doi10.1007/s10236-020-01382-4
dc.identifier.urihttp://hdl.handle.net/10754/664169
dc.description.abstractBayesian inference with coordinate transformations and polynomial chaos for a Gaussian process with a parametrized prior covariance model was introduced in Sraj et al. (Comput Methods Appl Mech Eng 298:205–228, 2016a) to enable and infer uncertainties in a parameterized prior field. The feasibility of the method was successfully demonstrated on a simple transient diffusion equation. In this work, we adopt a similar approach to infer a spatially varying Manning’s n field in a coastal ocean model. The idea is to view the prior on the Manning’s n field as a stochastic Gaussian field, expressed through a covariance function with uncertain hyper-parameters. A generalized Karhunen-Loève (KL) expansion, which incorporates the construction of a reference basis of spatial modes and a coordinate transformation, is then applied to the prior field. To improve the computational efficiency of the method proposed in Sraj et al. (Comput Methods Appl Mech Eng 298:205–228, 2016a), we propose to use two polynomial chaos expansions to (i) approximate the coordinate transformation and (ii) build a cheap surrogate of the large-scale advanced circulation (ADCIRC) numerical model. These two surrogates are used to accelerate the Bayesian inference process using a Markov chain Monte Carlo algorithm. Water elevation data are inverted within an observing system simulation experiment framework, based on a realistic ADCIRC model, to infer the KL coordinates and hyper-parameters of a reference 2D Manning’s field. Our results demonstrate the efficiency of the proposed approach and suggest that including the hyper-parameter uncertainties greatly enhances the inferred Manning’s n field, compared with using a covariance with fixed hyper-parameters.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/10.1007/s10236-020-01382-4
dc.rightsArchived with thanks to Ocean Dynamics
dc.titleBayesian inference of spatially varying Manning’s n coefficients in an idealized coastal ocean model using a generalized Karhunen-Loève expansion and polynomial chaos
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalOcean Dynamics
dc.rights.embargodate2021-07-01
dc.eprint.versionPost-print
dc.contributor.institutionLaboratoire d’Informatique pour la Mecanique et les Sciences de l’Ingénieur, Paris, France
dc.contributor.institutionUniversity of Texas at Austin, Austin, TX, USA
kaust.personSiripatana, Adil
kaust.personKnio, Omar
kaust.personHoteit, Ibrahim
dc.date.accepted2020-05-12
dc.identifier.eid2-s2.0-85087383200
dc.date.published-online2020-07-01
dc.date.published-print2020-08


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