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

We present neural inverse space mapping (NISM) optimization for electromag- netics-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 extrac- tion, 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 algo- rithm with neural space mapping (NSM) optimization. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 136 -147, 2003.

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