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    Gradient-based estimation of Manning's friction coefficient from noisy data

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    Type
    Article
    Authors
    Calo, Victor M. cc
    Collier, Nathan
    Gehre, Matthias
    Jin, Bangti
    Radwan, Hany G.
    Santillana, Mauricio
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Science and Engineering Program
    Environmental Science and Engineering Program
    Numerical Porous Media SRI Center (NumPor)
    Physical Science and Engineering (PSE) Division
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2013-01
    Preprint Posting Date
    2012-04-08
    Permanent link to this record
    http://hdl.handle.net/10754/562559
    
    Metadata
    Show full item record
    Abstract
    We study the numerical recovery of Manning's roughness coefficient for the diffusive wave approximation of the shallow water equation. We describe a conjugate gradient method for the numerical inversion. Numerical results for one-dimensional models are presented to illustrate the feasibility of the approach. Also we provide a proof of the differentiability of the weak form with respect to the coefficient as well as the continuity and boundedness of the linearized operator under reasonable assumptions using the maximal parabolic regularity theory. © 2012 Elsevier B.V. All rights reserved.
    Citation
    Calo, V. M., Collier, N., Gehre, M., Jin, B., Radwan, H., & Santillana, M. (2013). Gradient-based estimation of Manning’s friction coefficient from noisy data. Journal of Computational and Applied Mathematics, 238, 1–13. doi:10.1016/j.cam.2012.08.004
    Sponsors
    This work was initiated while V.M.C. was a Visiting Professor at the Institute for Applied Mathematics and Computational Science (IAMCS), Texas A&M University, College Station. The work of M.G. was carried out during his visit at IAMCS. They would like to thank the institute for the kind hospitality and support. The work of B.J. is supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
    Publisher
    Elsevier BV
    Journal
    Journal of Computational and Applied Mathematics
    DOI
    10.1016/j.cam.2012.08.004
    arXiv
    1204.1709
    Additional Links
    http://arxiv.org/abs/arXiv:1204.1709v1
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.cam.2012.08.004
    Scopus Count
    Collections
    Articles; Environmental Science and Engineering Program; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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