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Description
The dataset was generated using a procedure for expedited globalized parameter adjustment of microwave passives. The search process was embedded in a surrogate-assisted machine learning framework operating in a dimensionality-restricted domain, spanned by the parameter space directions being of importance in terms of their effects on the circuit characteristic variability.
Extensive comparisons with several state-of-the-art routines, including a bio-inspired algorithm and an ML scheme operating within the original parameter space, indicated competitive efficacy regarding the quality of the rendered designs and CPU efficiency. The CPU savings achieved due to dimensionality reduction were as high as 50%.
Dataset file
metadata_p1_complete.pdf
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File details
- License:
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CC BYAttribution
Details
- Year of publication:
- 2025
- Verification date:
- 2025-03-17
- Dataset language:
- English
- DOI:
- DOI ID 10.34808/5vp4-vj58 open in new tab
- Funding:
- Verified by:
- Gdańsk University of Technology
Keywords
References
- publication Optimization of Microwave Components Using Machine Learning and Rapid Sensitivity Analysis
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