Neural network model for the efficient calculation of Green's functions in layered media

Abstract

In this article, neural networks are employed for fast and efficient calculation of Green's functions in a layered medium. Radial basis function networks (RBFNs) are effectively trained to estimate the coefficients and the exponents that represent a Green's function in the discrete complex image method (DCIM). Results show very good agreement with the DCIM, and the trained RBFNs are very fast compared with the corresponding DCIM. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 128–135, 2003.