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dc.contributor.authorLitvinenko, Alexander
dc.contributor.authorYucel, Abdulkadir
dc.contributor.authorBagci, Hakan
dc.contributor.authorOppelstrup, Jesper
dc.contributor.authorMichielssen, Eric
dc.contributor.authorTempone, Raul
dc.date.accessioned2021-02-24T08:37:18Z
dc.date.available2021-02-24T08:37:18Z
dc.date.issued2021-01-25
dc.date.submitted2020-06-11
dc.identifier.citationLitvinenko, A., Yucel, A., Bagci, H., Oppelstrup, J., Michielssen, E., & Tempone, R. (2021). MLMC method to estimate propagation of uncertainties in electromagnetic fields scattered from objects of uncertain shapes. PAMM, 20(1). doi:10.1002/pamm.202000064
dc.identifier.issn1617-7061
dc.identifier.issn1617-7061
dc.identifier.doi10.1002/pamm.202000064
dc.identifier.urihttp://hdl.handle.net/10754/667651
dc.description.abstractWe estimate the propagation of uncertainties in electromagnetic wave scattering problems. The computational domain is a dielectric object with uncertain shape. Since classical Monte Carlo (MC) method is too expensive, we suggest to use a modified multilevel Monte Carlo (MLMC) method. This method uses a hierarchy of spatial meshes and optimally balances the statistical and discretisation errors. MLMC performs most of the simulations using low-fidelity models and only a few simulations using high-fidelity models. As a result, the final computational cost is becoming significantly smaller.
dc.description.sponsorshipThis research was supported by King Abdullah University of Science and Technology and the Alexander von Humboldt Foundation.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1002/pamm.202000064
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleMLMC method to estimate propagation of uncertainties in electromagnetic fields scattered from objects of uncertain shapes
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputational Electromagnetics Laboratory
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentStochastic Numerics Research Group
dc.identifier.journalPAMM
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionRWTH Aachen Kackertstr. 9 Aachen Germany
dc.contributor.institutionSveNanyang Technological University Singapore
dc.contributor.institutionKTH Royal Institute of Technology Stockholm Sweden
dc.contributor.institutionUniversity of Michigan USA
dc.identifier.volume20
dc.identifier.issue1
kaust.personBagci, Hakan
kaust.personTempone, Raul
dc.date.accepted2020-11-24
refterms.dateFOA2021-02-24T08:38:31Z


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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.