Numerical and experimental generated data during project https://doi.org/10.1109/TMTT.2023.3318702

Description

The dataset was generated using a procedure for the globalized design of microwave devices, which employs simplex-based regression models built at the level of operating parameters and performance figures of the circuit of interest. The computational efficiency of our technique stems from the topological simplicity of the surrogate and its updating rules, as well as the nearly linear dependence between the circuit dimensions and its operational parameters. Furthermore, the updating rules of the regression model guarantee convergence of the optimization process. The global search capability of our technique is validated through repetitive optimization of three microstrip circuits. A perfect success rate is demonstrated along with a low (average) computational cost of just about 80 EM analyses of the circuit at hand.

Dataset file

metadata_p24_complete.pdf

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Attribution

Details

Year of publication:

2025

Verification date:

2025-03-13

Dataset language:

English

DOI:

10.34808/tpwa-cx63

Funding:

Unsupervised specification-driven design of antenna structures using artificial intelligence and machine learning

Verified by:

Gdańsk University of Technology

Keywords

References

publication Globalized Parametric Optimization of Microwave Passive Components Using Simplex-Based Surrogates

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Authors

Sławomir Kozieł prof. dr inż.
Politechnika Gdańska - Katedra Systemów Mikroelektronicznych
orcid number 0000-0002-9063-2647open in new tab
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