Space mapping is a recognized method for speeding up electromagnetic (EM) optimization. Existing space-mapping approaches belong to the class of surrogate-based optimization methods. This paper proposes a cognition-driven formulation of space mapping that does not require explicit surrogates. The proposed method is applied to EM-based filter optimization. The new technique utilizes two sets of intermediate feature space parameters, including feature frequency parameters and ripple height parameters. The design variables are mapped to the feature frequency parameters, which are further mapped to the ripple height parameters. By formulating the cognition-driven optimization directly in the feature space, our method increases optimization efficiency and the ability to avoid being trapped in local minima. The technique is suitable for design of filters with equal-ripple responses. It is illustrated by two microwave filter examples.Index Terms-Cognition-driven design, computer-aided design (CAD), electromagnetic (EM) optimization, microwave filters, modeling, space mapping (SM).

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Conventional EM optimization aims to use fewest possible fine model evaluations to increase the speed of optimization. In this work, we propose to use a large number of fine model evaluations to achieve an overall speedup. A large number of fine model evaluations allows us to build a surrogate model valid in a large neighborhood. In the proposed technique, these valid surrogate models are used to achieve large and effective optimization updates, thereby resulting in fewer iterations of the optimization process. Valid surrogate models uses many fine model evaluations which are realized in parallel using hybrid distributed shared memory computing platforms. Parallel computation of large number of fine model evaluations reduces the major computational time required for constructing a surrogate model. Furthermore, we exploit trust region algorithms to guarantee convergence and to re-define the fine model evaluation range in each iteration of the proposed optimization algorithm. The proposed technique aims to increase the speed of gradient based EM optimization when no coarse model (e.g., empirical or equivalent circuits) is available. Three typical examples are used to illustrate the proposed technique.

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