Artificial Neural Networks (ANN) are very convenient in modeling high-dimensional and highly nonlinear components, as those found in the microwave and high frequency arena, due to their ability to learn and generalize from data, their non-linear processing nature, and their massively parallel structure. In modeling high frequency components the learning data is usually obtained from a detailed or “fine” model (EM simulator or measurements). This is generally very time consuming because the simulation/measurements must be performed for many combinations of different values of input parameters. This is the main drawback of classical ANN modeling. Without sufficient learning samples, the neural models may not be reliable. Several innovative strategies to develop neuromodels take advantage of empirical or “coarse” models already available (circuit-equivalent models and analytical formulas). Here we describe space mapping based neuromodeling of high frequency circuits.