Parallel Space-Mapping Approach to EM Optimization

Abstract

Space mapping (SM) is a recognized method for speeding up electromagnetic (EM) optimization. Existing SM approaches are mostly based on sequential computation mechanism. This paper proposes a parallel SM method for EM optimization. In the proposed method, the surrogate model developed in each iteration is trained to match the fine model at multiple points simultaneously. Multi-point training and SM enables the surrogate model to be valid in a larger neighborhood than that in standard SM. The proposed formulation of multi-point surrogate model training is inherently suited to and implemented through parallel computation. This includes multiple fine model evaluation in parallel and multi-point surrogate training using a parallel algorithm. Our proposed method further reduces the number of SM iterations and speeds up the optimization process in comparison with the standard SM. This technique is illustrated by three microwave filter examples.