A.G. Tijhuis

Higher-order statistics for stochastic electromagnetic interactions: applications to a thin-wire frame

Uncertainties in an electromagnetic observable, that arise from uncertainties in geometric and electromagnetic parameters of an interaction configuration, are here characterized by combining computable higher-order moments of the observable with higher-order Chebychev inequalities. This allows for the estimation of the range of the observable by rigorous confidence intervals. The estimated range is then combined with the maximum-entropy principle to arrive at an efficient and reliable estimation of the probability density function of the observable. The procedure is demonstrated for the case of the induced voltage of a thin-wire frame that has a random geometry, is connected to a random load, and is illuminated by a random incident field.

Modelling the interaction of stochastic systems with electromagnetic fields

The analysis presented in this paper, shows how the basic statistics in the interaction of stochastic systems with electromagnetic fields, i.e., the average and the variance of linear observables, can be computed from the probability distributions defining the stochastic geometry. The most difficult part in this computation is the construction of the covariance operator which requires the solution of a series of integral equations over the average geometry. The RHSs of these integral equations are determined by an expansion of the covariance operator of a regular field trace on the stochastic geometry. This determines the computational complexity of the probabilistic approach. In the presentation of this paper, some numerical experiments on concrete examples are shown, and the computational complexity of the probabilistic approach are investigated whether it also determines the minimal computational effort of the statistical approach

Computational Techniques for Antenna Engineering

In this paper, a two-stage approach is proposed for using computational electromagnetics in antenna engineering. First, stochastic optimization techniques are used in combination with approximate models. Second, line-search techniques are combined with full-wave modeling. The second stage is considered in detail; both the acceleration of the underlying field computations and the implementation of the optimization are discussed.

Preconditioned Finite-Difference Frequency-Domain for Modelling Periodic Dielectric Structures - Comparisons with FDTD

Finite-difference techniques are very popular and versatile numerical tools in computational electromagnetics. In this paper, we propose a preconditioned finite-difference frequency-domain method (FDFD) to model periodic structures in 2D and 3D. The preconditioner follows from a modal decoupling approximation. Its use involves discrete Fourier transforms (via FFTs). We have set FDFD against an FDTD package for a typical test case, with identical spatial grids. We have observed that computation times for a full frequency sweep are comparable, while the accuracy is slightly better with FDFD.