Neural inverse space mapping (NISM) optimization for EM-based microwave design

We present neural inverse space mapping (NISM) optimization for electromagnetics-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. It employs a simple statistical parameter extraction procedure. 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.