Response surface space mapping for electromagnetic optimization

Dorica, M.; Giannacopoulos, Dennis D.

doi:10.1109/tmag.2006.872018

Cited by 47 publications

(12 citation statements)

References 11 publications

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“…This, in combination with the multipoint parameter extraction (4), ensures that (6) and (7) may be satisfied approximately. Moreover, condition (6) can be easily enforced by means of the output space mapping [3] using the surrogate (8) with obtained by (4) and (9) Numerical results presented in Section IV demonstrate that the trust-region-enhanced (output) space-mapping algorithm (8) and (9) indeed exhibits improvement over the standard algorithm.…”

Section: Trust-region Enhanced Space-mapping Algorithmmentioning

confidence: 97%

The state of the art of microwave CAD: EM-based optimization and modeling

Cheng

Koziel

Bakr

et al. 2010

Int J RF and Microwave Comp Aid Eng

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Abstract-Convergence is a well-known issue for standard space-mapping optimization algorithms. It is heavily dependent on the choice of coarse model, as well as the space-mapping transformations employed in the optimization process. One possible convergence safeguard is the trust region approach where a surrogate model is optimized in a restricted neighborhood of the current iteration point. In this paper, we demonstrate that although formal conditions for applying trust regions are not strictly satisfied for space-mapping surrogate models, the approach improves the overall performance of the space-mapping optimization process. Further improvement can be realized when approximate fine model Jacobian information is exploited in the construction of the space-mapping surrogate. A comprehensive numerical comparison between standard and trust-region-enhanced space mapping is provided using several examples of microwave design problems.Index Terms-Computer-aided design (CAD), electromagnetic (EM) optimization, microwave design, space mapping, trust-region methods.

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Section: Trust-region Enhanced Space-mapping Algorithmmentioning

confidence: 97%

The state of the art of microwave CAD: EM-based optimization and modeling

Cheng

Koziel

Bakr

et al. 2010

Int J RF and Microwave Comp Aid Eng

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“…Design follows methodology described in Section IV, and it starts from x in ¼ [10,15,30,30,20,6,8,100, 100] T mm; its response is shown in Figure 7. The antenna models are evaluated with the CST MWS transient solver: the coarse-discretization model with 55,000 mesh cells at x in (evaluation time 110 seconds), and the fine model with 1,240,896 mesh cells at x in (evaluation time 100 minutes).…”

Section: B Dual-band Double-ring Antennamentioning

confidence: 99%

Rapid design optimization of antennas using space mapping and response surface approximation models

Koziel

Ogurtsov

2011

Int J RF and Microwave Comp Aid Eng

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A computationally efficient method for design optimization of antennas is discussed. It combines space mapping, used as the optimization engine, and response surface approximation, used to create the fast surrogate model of the optimized antenna. The surrogate is configured from the response of the coarse-mesh electromagnetic model of the antenna, and implemented through kriging interpolation. We provide a comprehensive numerical verification of this technique as well as demonstrate its capability to yield a satisfactory design after a few full-wave simulations of the original structure.

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“…Space mapping (SM) [6][7][8][9][10][11][12][13][14][15][16][17], tuning procedures [18][19][20][21][22], as well as various response correction methods [23,24] are examples of very efficient approaches of this kind used in microwave engineering.…”

Section: Introductionmentioning

confidence: 99%

Reliable Simulation-Driven Design Optimization of Microwave Structures Using Manifold Mapping

Koziel¹,

Ciaurri²

2010

PIER B

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Abstract-A computationally efficient surrogate-based framework for reliable simulation-driven design optimization of microwave structures is described. The key component of our algorithm is manifold mapping, a response correction technique that aligns the coarse model (computationally cheap representation of the structure under consideration) with the accurate but CPU-intensive (fine) model of the optimized device. The parameters of the manifold mapping surrogate are explicitly calculated based on the fine model data accumulated during the optimization process. Also, manifold mapping does not use any extractable parameters, which makes it easy to implement. Robustness and excellent convergence properties of the proposed algorithm are demonstrated through the design of several microwave devices including microstrip filters and a planar antenna.

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Response surface space mapping for electromagnetic optimization

Cited by 47 publications

References 11 publications

The state of the art of microwave CAD: EM-based optimization and modeling

The state of the art of microwave CAD: EM-based optimization and modeling

Rapid design optimization of antennas using space mapping and response surface approximation models

Reliable Simulation-Driven Design Optimization of Microwave Structures Using Manifold Mapping

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