An FMM-FFT Accelerated SIE Simulator for Analyzing EM Wave Propagation in Mine Environments Loaded With Conductors

Yücel, Abdulkadir C.; Sheng, Weitian; Zhou, Chenming; Liu, Yang; Bağcı, Hakan; Michielssen, E.

doi:10.1109/jmmct.2018.2802420

Cited by 14 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The deterministic simulations required by the CMLMC method to compute the samples at different levels are carried out using the Poggio-Miller-Chan-Harrington-Wu-Tsai surface integral equation (PMCHWT-SIE) solver [39]. The PMCHWT-SIE is discretized using the method of moments (MoM) and the iterative solution of the resulting matrix system is accelerated using a (parallelized) fast multipole method (FMM) -fast Fourier transform (FFT) scheme (FMM-FFT) [9,11,49,50,57,66,68]. Numerical results, which demonstrate the accuracy, efficiency, and convergence of the proposed computational framework, are presented.…”

Section: Rotationmentioning

confidence: 99%

“…To minimize the computational and storage cost while executing the MLMC algorithm a fast multipole method (FMM)-fast Fourier transform (FFT) scheme is used. A detailed formulation of FMM and its extension FMM-FTT can be found in [9,11,49,50,57,66,68]. The scheme encloses the scatterer in a fictitious box that is embedded into a uniform grid of smaller boxes.…”

Section: Electromagnetic Solvermentioning

confidence: 99%

“…Note that the computational cost estimate provided above is obtained under the assumption that the ratio of the wavelength to the (average) element edge length stays the same as N is decreased/increased [49,50,57,66,68]. Within the CMLMC algorithm, the mesh is refined from one level to the next while the frequency is kept constant.…”

Section: Electromagnetic Solvermentioning

confidence: 99%

“…The interactions between basis and test functions in far-field pairs are represented using the radiation patterns of basis and test functions (sampled over a solid angle of a sphere) and a translation operator and their contributions to Z Ī are computed using the FMM-FFT scheme. The computational cost and memory requirement of the FMM-FFT accelerated iterative solution scale as O(N iter N 4/3 log 2/3 N ) and O(N 4/3 log 2/3 N ), respectively [66,68]. Note that these estimates are higher than those of the multilevel FMM [46,47]; the FMM-FFT scheme is preferred here since its parallel implementation is significantly simpler [12, 13, 16, 35-37, 40, 49, 50, 57, 66, 68].…”

Section: Electromagnetic Solvermentioning

confidence: 99%

See 3 more Smart Citations

Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method

Litvinenko

Yücel

Bağcı

et al. 2019

IEEE J. Multiscale Multiphys. Comput. Tech.

Self Cite

View full text Add to dashboard Cite

Computational tools for characterizing electromagnetic scattering from objects with uncertain shapes are needed in various applications ranging from remote sensing at microwave frequencies to Raman spectroscopy at optical frequencies. Often, such computational tools use the Monte Carlo (MC) method to sample a parametric space describing geometric uncertainties. For each sample, which corresponds to a realization of the geometry, a deterministic electromagnetic solver computes the scattered fields. However, for an accurate statistical characterization the number of MC samples has to be large. In this work, to address this challenge, the continuation multilevel Monte Carlo (CMLMC) method is used together with a surface integral equation solver. The CMLMC method optimally balances statistical errors due to sampling of the parametric space, and numerical errors due to the discretization of the geometry using a hierarchy of discretizations, from coarse to fine. The number of realizations of finer discretizations can be kept low, with most samples computed on coarser discretizations to minimize computational cost. Consequently, the total execution time is significantly reduced, in comparison to the standard MC scheme. and vegetation [53,55] to enhancing Raman spectroscopy using metallic nanoparticles [51,58]. When the target size is comparable to or larger than the wavelength at the operation frequency, the scattered field is a strong function of the target shape. However, in many of the applications, whether the target is a (vegetated) rough surface or a nanoparticle, its exact shape may not be known and has to be parameterized using a stochastic/statistical model. Consequently, computational tools, which are capable of generating statistics of a quantity of interest (QoI) (RCS or SCS in this case) given a geometry described using random variables/parameters, are required.These computational tools often use the Monte Carlo (MC) method [18,27,42,45,48,54,56]. The MC method is non-intrusive and straightforward to implement; therefore, its incorporation with an existing deterministic EM solver is rather trivial. However, the traditional MC method has an error convergence rate of O(N −1/2 ) [41], where N is the number of samples in the parametric space used for describing the geometry. Provided more regularity of the QoI w.r.t. the geometry parameters, quasi-MC methods may have a better convergence rate, O(N −1 ) with a multiplicative log-term that depends on the number of parameters [29,41]. Both the traditional MC and quasi-MC methods require large N to yield accurate statistics of the QoI and are computationally expensive since the function evaluation at each sampling point, which corresponds to the computation of the QoI for one realization of the deterministic problem, requires the execution of a simulation. For practical real-life scattering scenarios, each of these simulations may take a few hours, if not a few days.In recent years, schemes that make use of surrogate models have received significant attention as po...

show abstract

Section: Rotationmentioning

confidence: 99%

Section: Electromagnetic Solvermentioning

confidence: 99%

Section: Electromagnetic Solvermentioning

confidence: 99%

Section: Electromagnetic Solvermentioning

confidence: 99%

See 2 more Smart Citations

Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method

Litvinenko

Yücel

Bağcı

et al. 2019

IEEE J. Multiscale Multiphys. Comput. Tech.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We use the Poggio-Miller-Chan-Harrington-Wu-Tsai surface integral equation (PMCHWT-SIE) [1]. The PMCHWT-SIE is discretized using the method of moments (MoM) and the iterative solution of the resulting matrix system is accelerated using a (parallelized) fast multipole method (FMM) -fast Fourier transform (FFT) scheme (FMM-FFT) [9]. Even with these modern numerical techniques the computational time for real-life problems may vary from a few hours to a few days.…”

mentioning

confidence: 99%

MLMC method to estimate propagation of uncertainties in electromagnetic fields scattered from objects of uncertain shapes

Litvinenko

Yücel

Bağcı

et al. 2021

Proc Appl Math and Mech

Self Cite

View full text Add to dashboard Cite

We 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.

show abstract

A Multi-trace Surface Integral Equation Solver to Analyze Electromagnetic Field Interactions on Graphene-based Devices

Zhao

Chen

et al. 2022

2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)

Self Cite

View full text Add to dashboard Cite

A multi-trace surface integral equation (MT-SIE) solver is proposed to analyze electromagnetic field interactions on graphene-based devices. This solver decomposes the computation domain into an exterior subdomain (the background medium where the device resides in) and an interior subdomain that represents the device's dielectric substrate. The infinitesimally thin graphene sheet partially covers the interface between these two subdomains. On the graphene surface and the rest of this interface, resistive Robin transmission conditions (formulated for the first time in this work) and the traditional Robin transmission conditions are used to "connect" these two subdomains, respectively.In each subdomain, the electric and magnetic field equations are used as the governing equations. Various numerical examples are presented to demonstrate the accuracy and the applicability of the proposed MT-SIE solver.

show abstract

An FMM-FFT Accelerated SIE Simulator for Analyzing EM Wave Propagation in Mine Environments Loaded With Conductors

Cited by 14 publications

References 31 publications

Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method

Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method

MLMC method to estimate propagation of uncertainties in electromagnetic fields scattered from objects of uncertain shapes

A Multi-trace Surface Integral Equation Solver to Analyze Electromagnetic Field Interactions on Graphene-based Devices

Contact Info

Product

Resources

About