Abstract:Abstract-Numerical analysis of a generalized form of the recently developed electric and magnetic current combined field integral equation (JM-CFIE) for electromagnetic scattering by homogeneous dielectric and composite objects is presented. This new formulation contains a similar coupling parameter α as CFIE contains in the case of perfectly conducting objects. Two alternative JM-CFIE(α) formulations are introduced and their numerical properties (solution accuracy and convergence of iterative Krylov subspace … Show more
“…where α ∈ ½0; 1 is a combination parameter [18],n is the unit normal vector at the observation point r, and…”
Section: Matrix Equations Obtained From Jmcfiementioning
confidence: 99%
“…This paper present an efficient parallelization of MLFMA for the solution of large-scale problems involving threedimensional homogeneous dielectric objects. The problems are formulated with the electric and magnetic current combined-field integral equation (JMCFIE) [17][18][19][20] and discretized with the Rao-Wilton-Glisson (RWG) [21] functions on planar triangles. The resulting dense matrix equations are solved iteratively by using a parallel implementation of MLFMA on distributed-memory architectures.…”
Fast and accurate solutions of large-scale electromagnetics problems involving homogeneous dielectric objects are considered. Problems are formulated with the electric and magnetic current combined-field integral equation and discretized with the Rao-Wilton-Glisson functions. Solutions are performed iteratively by using the multilevel fast multipole algorithm (MLFMA). For the solution of large-scale problems discretized with millions of unknowns, MLFMA is parallelized on distributed-memory architectures using a rigorous technique, namely, the hierarchical partitioning strategy. Efficiency and accuracy of the developed implementation are demonstrated on very large problems involving as many as 100 million unknowns.
“…where α ∈ ½0; 1 is a combination parameter [18],n is the unit normal vector at the observation point r, and…”
Section: Matrix Equations Obtained From Jmcfiementioning
confidence: 99%
“…This paper present an efficient parallelization of MLFMA for the solution of large-scale problems involving threedimensional homogeneous dielectric objects. The problems are formulated with the electric and magnetic current combined-field integral equation (JMCFIE) [17][18][19][20] and discretized with the Rao-Wilton-Glisson (RWG) [21] functions on planar triangles. The resulting dense matrix equations are solved iteratively by using a parallel implementation of MLFMA on distributed-memory architectures.…”
Fast and accurate solutions of large-scale electromagnetics problems involving homogeneous dielectric objects are considered. Problems are formulated with the electric and magnetic current combined-field integral equation and discretized with the Rao-Wilton-Glisson functions. Solutions are performed iteratively by using the multilevel fast multipole algorithm (MLFMA). For the solution of large-scale problems discretized with millions of unknowns, MLFMA is parallelized on distributed-memory architectures using a rigorous technique, namely, the hierarchical partitioning strategy. Efficiency and accuracy of the developed implementation are demonstrated on very large problems involving as many as 100 million unknowns.
“…Similar to CFIE for conducting objects, a Galerkin discretization of JMCFIE results in well-tested identity operators, which lead to very efficient iterative solutions [33], but may reduce the accuracy of the results. Therefore, the combination parameter in JMCFIE is extremely important for the tradeoff between the accuracy and the efficiency [32], [36].…”
“…In spite of its apparent generality, it requires a variety of special cases to produce accurate results in all situations. Different formulations are used for different types of materials; conductors [4] and dielectrics [5,6] singly or together [2,3,7,8]; and even for a single type of material accuracy can be quite variable [8][9][10][11] over a range of values of material properties.…”
In our model, we first calculate the conditional expectation for the complete-data likelihood, Q(Γ, Γ k−1) = E Φ|D,Γ k {log P (D, Φ|Γ)} = E Φ|D,Γ k { N ∑ i=1 log P (D i , ϕ i |Γ)} = N ∑ i=1
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