Mostafa Refat A. Ismail

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—We introduce the idea of implicit space mapping (ISM) and show how it relates to the well-established (explicit) space mapping between coarse and fine device models. Through comparison, a general space mapping concept is proposed. A simple algorithm based on the novel ISM concept is implemented. It is illustrated on a contrived " cheese-cutting problem "(More)
— For the first time, we present modeling of microwave circuits using artificial neural networks (ANN's) based on space-mapping (SM) technology. SM-based neuromodels decrease the cost of training, improve generalization ability, and reduce the complexity of the ANN topology with respect to the classical neuromodeling approach. Five creative techniques are(More)
—A comprehensive framework to engineering device modeling, which we call generalized space mapping (GSM) is introduced in this paper. GSM permits many different practical implementations. As a result, the accuracy of available empirical models of microwave devices can be significantly enhanced. We present three fundamental illustrations: a basic(More)
—We propose, for the first time, neural space-mapping (NSM) optimization for electromagnetic-based design. NSM optimization exploits our space-mapping (SM)-based neuromod-eling techniques to efficiently approximate the mapping. A novel procedure that does not require troublesome parameter extraction to predict the next point is proposed. The initial mapping(More)
—We present a novel design framework for microwave circuits. We calibrate coarse models (circuit based models) to align with fine models (full-wave electromagnetic simulations) by allowing some preassigned parameters (which are not used in optimization) to change in some components of the coarse model. Our expanded space-mapping design-framework (ESMDF)(More)
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 extraction , or(More)
We present a new computer-aided modeling methodology to develop physics-based models for passive components. We coherently integrate full-wave electromagnetic simulators, artificial neural networks, multivariable rational functions, dimensional analysis , and frequency mapping to establish broadband models. We consider both frequency-independent and(More)
— We present a simple new approach to EM-based microwave modeling and design. It is a special case of a novel concept we call Implicit Space Mapping. We propose to calibrate a suitable coarse model against a fine model (full wave EM simulation) by relaxing certain coarse model preassigned parameters. Our algorithm updates these preassigned parameters(More)