Computation of Electromagnetic Fields Scattered From Dielectric Objects of Uncertain Shapes Using MLMC Center for Uncertainty

Handle URI:
http://hdl.handle.net/10754/623622
Title:
Computation of Electromagnetic Fields Scattered From Dielectric Objects of Uncertain Shapes Using MLMC Center for Uncertainty
Authors:
Litvinenko, Alexander ( 0000-0001-5427-3598 ) ; Haji-Ali, Abdul Lateef ( 0000-0002-6243-0335 ) ; Uysal, Ismail Enes ( 0000-0003-4053-769X ) ; Ulku, Huseyin Arda ( 0000-0003-4682-3902 ) ; Oppelstrup, Jesper; Tempone, Raul Fidel ( 0000-0003-1967-4446 ) ; Bagci, Hakan ( 0000-0003-3867-5786 )
Abstract:
Simulators capable of computing scattered fields from objects of uncertain shapes are highly useful in electromagnetics and photonics, where device designs are typically subject to fabrication tolerances. Knowledge of statistical variations in scattered fields is useful in ensuring error-free functioning of devices. Oftentimes such simulators use a Monte Carlo (MC) scheme to sample the random domain, where the variables parameterize the uncertainties in the geometry. At each sample, which corresponds to a realization of the geometry, a deterministic electromagnetic solver is executed to compute the scattered fields. However, to obtain accurate statistics of the scattered fields, the number of MC samples has to be large. This significantly increases the total execution time. In this work, to address this challenge, the Multilevel MC (MLMC) scheme is used together with a (deterministic) surface integral equation solver. The MLMC achieves a higher efficiency by “balancing” the statistical errors due to sampling of the random domain and the numerical errors due to discretization of the geometry at each of these samples. Error balancing results in a smaller number of samples requiring coarser discretizations. Consequently, total execution time is significantly shortened.
KAUST Department:
ECRC, KAUST; SRI Center for Uncertainty Quantification in Computational Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Electrical Engineering, KAUST; Electrical Engineering, KAUST; SRI Center for Uncertainty Quantification in Computational Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Electrical Engineering, KAUST
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
Issue Date:
5-Jan-2015
Type:
Poster
Sponsors:
KAUST SRI-UQ
Additional Links:
https://sri-uq.kaust.edu.sa/Pages/UQAnnualWorkshop2015.aspx
Appears in Collections:
Posters

Full metadata record

DC FieldValue Language
dc.contributor.authorLitvinenko, Alexanderen
dc.contributor.authorHaji-Ali, Abdul Lateefen
dc.contributor.authorUysal, Ismail Enesen
dc.contributor.authorUlku, Huseyin Ardaen
dc.contributor.authorOppelstrup, Jesperen
dc.contributor.authorTempone, Raul Fidelen
dc.contributor.authorBagci, Hakanen
dc.date.accessioned2017-05-16T08:20:34Z-
dc.date.available2017-05-16T08:20:34Z-
dc.date.issued2015-01-05-
dc.identifier.urihttp://hdl.handle.net/10754/623622-
dc.description.abstractSimulators capable of computing scattered fields from objects of uncertain shapes are highly useful in electromagnetics and photonics, where device designs are typically subject to fabrication tolerances. Knowledge of statistical variations in scattered fields is useful in ensuring error-free functioning of devices. Oftentimes such simulators use a Monte Carlo (MC) scheme to sample the random domain, where the variables parameterize the uncertainties in the geometry. At each sample, which corresponds to a realization of the geometry, a deterministic electromagnetic solver is executed to compute the scattered fields. However, to obtain accurate statistics of the scattered fields, the number of MC samples has to be large. This significantly increases the total execution time. In this work, to address this challenge, the Multilevel MC (MLMC) scheme is used together with a (deterministic) surface integral equation solver. The MLMC achieves a higher efficiency by “balancing” the statistical errors due to sampling of the random domain and the numerical errors due to discretization of the geometry at each of these samples. Error balancing results in a smaller number of samples requiring coarser discretizations. Consequently, total execution time is significantly shortened.en
dc.description.sponsorshipKAUST SRI-UQen
dc.relation.urlhttps://sri-uq.kaust.edu.sa/Pages/UQAnnualWorkshop2015.aspxen
dc.subjectrandom geometryen
dc.subjectMultilevel Monte Carloen
dc.subjectscattered fielden
dc.subjectuncertainties in geometryen
dc.subjectelectromagnetics and photonicsen
dc.titleComputation of Electromagnetic Fields Scattered From Dielectric Objects of Uncertain Shapes Using MLMC Center for Uncertaintyen
dc.typePosteren
dc.contributor.departmentECRC, KAUSTen
dc.contributor.departmentSRI Center for Uncertainty Quantification in Computational Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabiaen
dc.contributor.departmentElectrical Engineering, KAUSTen
dc.contributor.departmentElectrical Engineering, KAUSTen
dc.contributor.departmentSRI Center for Uncertainty Quantification in Computational Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabiaen
dc.contributor.departmentElectrical Engineering, KAUSTen
dc.conference.dateJanuary 2015en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)en
dc.conference.locationKAUST, B1en
dc.contributor.institutionKTH Royal Institute of Technology in Stockholmen
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