Fast methods for evaluating the electric field level in 2D(-)indoor environments

When estimating the electric field level in an indoor environment, the usual complexity of the geometry and its large electric size make it necessary to deal with asymptotic assumptions, also known as high frequency techniques. But, even with these assumptions, the computational complexity, and the CPU-time cost, can be very high. Considering this drawback, this paper proposes the implementation of a Neural Networks System for fast calculations of the Electric field in 2D-indoor environments.