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Author

Bagci, Hakan (16)

Ulku, Huseyin Arda (16)

Uysal, Ismail Enes (8)Sayed, Sadeed B (7)Haji Ali, Abdul Lateef (3)View MoreDepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division (15)Electrical Engineering Program (15)Physical Sciences and Engineering (PSE) Division (4)Applied Mathematics and Computational Science Program (3)Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ) (1)View MoreSubjectCEM (6)electromagnetics and photonics (1)Multilevel Monte Carlo (1)random geometry (1)scattered field (1)View MoreType
Poster (16)

Year (Issue Date)2016 (3)2015 (6)2014 (7)Item AvailabilityOpen Access (16)

Now showing items 1-10 of 16

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

Litvinenko, Alexander; Haji Ali, Abdul Lateef; Uysal, Ismail Enes; Ulku, Huseyin Arda; Oppelstrup, Jesper; Tempone, Raul; Bagci, Hakan (2015-01-05) [Poster]

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.

Transient Analysis of Electromagnetic Wave Interactions on Ferromagnetic Structures Using Landau-Lifshitz-Gilbert and Volume Integral Equations

Sayed, Sadeed B; Ulku, Huseyin Arda; Bagci, Hakan (2016-01-06) [Poster]

Analysis of Transient Electromagnetic Interactions on Nanodevices Using a Quantum-corrected Integral Equation Approach

Uysal, Ismail Enes; Ulku, Huseyin Arda; Bagci, Hakan (2016-01-06) [Poster]

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

Litvinenko, Alexander; Haji Ali, Abdul Lateef; Uysal, Ismail Enes; Ulku, Huseyin Arda; Tempone, Raul; Bagci, Hakan; Oppelstrup, Jesper (2015-01-07) [Poster]

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.

Analysis of Transient Electromagnetic Wave Interactions on Graphene Sheets Using Integral Equations

Shi, Yifei; Sandhu, Ali Imran; Li, Peng; Uysal, Ismail Enes; Ulku, Huseyin Arda; Bagci, Hakan (2015-01-07) [Poster]

An Explicit and Stable MOT Solver for Time Domain Volume Electric Field Integral Equation

Sayed, Sadeed B; Ulku, Huseyin Arda; Bagci, Hakan (2014-05-04) [Poster]

Stabilizing MOT Solution of TD-VIE for High-Contrast Scatterers using Accurate Extrapolation

Sayed, Sadeed B; Ulku, Huseyin Arda; Bagci, Hakan (2014-01-06) [Poster]

An Explicit and Stable MOT Solver for Time Domain Volume Electric Field Integral Equation

Sayed, Sadeed B; Ulku, Huseyin Arda; Bagci, Hakan (2014-01-06) [Poster]

Nyström-discretized Magnetic Field Integral Equation for 2D Electromagnetic Scattering

Al-Harthi, Noha A.; Ulku, Huseyin Arda; Yokota, Rio; Keyes, David E.; Bagci, Hakan (2014-05-04) [Poster]

Litvinenko, Alexander; Haji Ali, Abdul Lateef; Uysal, Ismail Enes; Ulku, Huseyin Arda; Oppelstrup, Jesper; Tempone, Raul; Bagci, Hakan (2016-01-06) [Poster]

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.

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