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

Handle URI:
http://hdl.handle.net/10754/624818
Title:
Computation of Electromagnetic Fields Scattered From Dielectric Objects of Uncertain Shapes Using MLMC
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 ( 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 [1]) 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:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Electrical Engineering Program; Physical Sciences and Engineering (PSE) Division; Computer, Electrical and Mathematical Sciences & Engineering (CEMSE)
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
Issue Date:
6-Jan-2016
Type:
Poster
Appears in Collections:
Posters; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)

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, Raulen
dc.contributor.authorBagci, Hakanen
dc.date.accessioned2017-06-08T06:32:28Z-
dc.date.available2017-06-08T06:32:28Z-
dc.date.issued2016-01-06-
dc.identifier.urihttp://hdl.handle.net/10754/624818-
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 [1]) 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.subjectCEMen
dc.titleComputation of Electromagnetic Fields Scattered From Dielectric Objects of Uncertain Shapes Using MLMCen
dc.typePosteren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)en
dc.conference.dateJanuary 5-10, 2016en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)en
dc.conference.locationKAUSTen
kaust.authorLitvinenko, Alexanderen
kaust.authorHaji Ali, Abdul Lateefen
kaust.authorUysal, Ismail Enesen
kaust.authorUlku, Huseyin Ardaen
kaust.authorOppelstrup, Jesperen
kaust.authorTempone, Raulen
kaust.authorBagci, Hakanen
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