The novel application of the method enables a large reduction of the absolute FDTD-PCE computations time. In this paper, the method is applied and adjusted for the first time to FDTD-PCE computations. The aforementioned method was introduced in where it was used with the uniform theory of diffraction (UTD) for worst-case analysis in a wireless telecommunication channel. It is based on the author’s analytical transformations and proper segmentation of the support of those design random variables. The author uses a method for a substantial reduction of the number of FDTD simulation runs required to compute PCE meta-models associated with the predefined nominal values of geometrical antenna design parameters.
Patch antennas are chosen as the subject of the scientific analysis in this paper because of the popularity of the patch antennas in the scientific literature concerning antennas, as well as because of a simple form of these antennas that is reflected in the time required for computation of training and testing data for the Artificial Neural Network.Įach FDTD simulation is a very time-consuming process, e.g., computing the mean and standard deviation of | S 11| may require 10 or more FDTD simulation runs for each set of nominal values of design random variables, depending on the size of the support of these random variables. The probability distributions of the design parameters are extracted from the measurement results obtained for a series of manufactured patch antenna arrays for three different frequencies in the C, X, and Ka bands. An analysis is performed for n257 and n258 frequency bands (24.5–28.7 GHz). For the first time, the author uses his analytical transformations to reduce the required number of highly time-consuming FDTD simulations for a given set of nominal values of the design parameters of the ordinary patch antenna. The Polynomial Chaos Expansion and FDTD computations are used to obtain the training and testing data for the Artificial Neural Network. The Artificial Neural Network is used to model the stochastic reflection coefficient of the antennas.
In the paper, the author deals with modeling the stochastic behavior of ordinary patch antennas in terms of the mean and standard deviation of their reflection coefficient | S 11| under the geometrical uncertainty associated with their manufacturing process.
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