Space mapping interpolating surrogates for highly optimized EM-based design of microwave devices

Cheng, Qingsha S.; Dakroury, S.A.; Hailu, Daniel M.; Madsen, Kaj; Mohamed, A.S.A.; Pedersen, Frank

doi:10.1109/mwsym.2004.1338878

Cited by 3 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also aim to align the surrogate with the fine-model response at the previous points in the iteration, as well as aim to have agreement between the Jacobians at the current point, i.e., (6) where and are the Jacobians of the surrogate and fine model at , respectively. The constants are determined in such a way that the alignment (5) holds and the requirements in (6) are satisfied as well as possible (in some sense to be specified).…”

Section: A Surrogatementioning

confidence: 99%

“…Since (5) is automatically satisfied by using (7), the aim is to ensure the matching (6). Thus, for finding , we aim to minimize the following set of residuals in some sense [6]:…”

Section: B Surface Fitting Approach For Parameter Extraction (Pe)mentioning

confidence: 99%

“…Highly accurate space-mapping interpolating surrogate (SMIS) models are built for use in gradient-based optimization [6]. The SMIS is required to match both the responses and derivatives of the fine model within a local region of interest.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Space-Mapping Interpolating Surrogate Algorithm for Highly Optimized EM-Based Design of Microwave Devices

Bandler

Hailu

Madsen

et al. 2004

IEEE Trans. Microwave Theory Techn.

Self Cite

View full text Add to dashboard Cite

Abstract-We justify and elaborate in detail on a powerful new optimization algorithm that combines space mapping (SM) with a novel output SM. In a handful of fine-model evaluations, it delivers for the first time the accuracy expected from classical direct optimization using sequential linear programming. Our new method employs a space-mapping-based interpolating surrogate (SMIS) framework that aims at locally matching the surrogate with the fine model. Accuracy and convergence properties are demonstrated using a seven-section capacitively loaded impedance transformer. In comparing our algorithm with major minimax optimization algorithms, the SMIS algorithm yields the same minimax solution within an error of 10 15 as the Hald-Madsen algorithm. A highly optimized six-section -plane waveguide filter design emerges after only four HFSS electromagnetic simulations, excluding necessary Jacobian estimations, using our algorithm with sparse frequency sweeps.Index Terms-Computer-aided design (CAD) algorithms, electromagnetics, filter design, interpolating surrogate, microwave modeling, optimization, output space mapping (OSM), space mapping (SM), surrogate modeling.

show abstract

Section: A Surrogatementioning

confidence: 99%

“…Since (5) is automatically satisfied by using (7), the aim is to ensure the matching (6). Thus, for finding , we aim to minimize the following set of residuals in some sense [6]:…”

Section: B Surface Fitting Approach For Parameter Extraction (Pe)mentioning

confidence: 99%