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. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 136–147, 2003.