2016
DOI: 10.1002/nme.5313
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Proper generalized decomposition solution of the parameterized Helmholtz problem: application to inverse geophysical problems

Abstract: This is the peer reviewed version of the following article: [Signorini, M., Zlotnik, S., and Díez, P. (2017) Proper generalized decomposition solution of the parameterized Helmholtz problem: application to inverse geophysical problems. Int. J. Numer. Meth. Engng, 109: 1085–1102. doi: 10.1002/nme.5313], which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/nme.5313/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Arc… Show more

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Cited by 24 publications
(27 citation statements)
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“…The strategy to deal with geometric parameters in the PGD solver was devised in [12] for Poisson problems and then combined with material parameters in [13] and [14] for heat and wave propagation problems. The fundamental idea is using a reference domain T and a parametric mapping to the physical domain Ω(µ).…”
Section: Stokes Flow In Domains With Parametric Geometry 411 Accounmentioning
confidence: 99%
“…The strategy to deal with geometric parameters in the PGD solver was devised in [12] for Poisson problems and then combined with material parameters in [13] and [14] for heat and wave propagation problems. The fundamental idea is using a reference domain T and a parametric mapping to the physical domain Ω(µ).…”
Section: Stokes Flow In Domains With Parametric Geometry 411 Accounmentioning
confidence: 99%
“…An approximate solution of the model can then be obtained for any value of the parameters and be used in an online phase, with cheap and fast computations on light computing platforms, in order to perform real‐time parametric or sensitivity analysis for optimization, inverse identification, or optimal control purposes. The PGD technique was successfully implemented for many applications with parametric analyses, including parameterized geometry . Here, we go one step further by coupling PGD with IGA.…”
Section: Introductionmentioning
confidence: 99%
“…The PGD technique was successfully implemented for many applications with parametric analyses, including parameterized geometry. 8,35,[50][51][52] Here, we go one step further by coupling PGD with IGA. PGD extra coordinates are then related to the coordinates and weights of control points (at the coarsest-mesh level), and separation of variables is introduced between space and geometry variables.…”
Section: Introductionmentioning
confidence: 99%
“…23,24 The construction of the PGD solution is made in an offline phase and leads to a cost-effective evaluation of the numerical model depending on parameters in the online inversion phase. 25,26 The issue of the model evaluation cost is emphasized in Bayesian inference where the posterior density has to be explored to derive useful information on parameter pdfs such as mean, standard deviation, maximum, and marginals. Those quantities require the computation of multidimensional integrals over the parametric space.…”
Section: Introductionmentioning
confidence: 99%