Descripción:
For the first time, we present Neural Inverse
Space Mapping (NISM) optimization for EM-based design of
microwave structures. The inverse of the mapping from the
fine to the coarse model parameter spaces is exploited for the
first time in a Space Mapping algorithm. NISM optimization
does not require: up-front EM simulations, multipoint
parameter extraction or frequency mapping. The inverse of
the mapping is approximated by a neural network whose
generalization performance is controlled through a network
growing strategy. We contrast our new algorithm with
Neural Space Mapping (NSM) optimization.