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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Microwave Theory and Techniques
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
