Numerical and experimental generated data during project https://doi.org/10.1038/s41598-024-72478-w - Open Research Data - Bridge of Knowledge

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Numerical and experimental generated data during project https://doi.org/10.1038/s41598-024-72478-w

Description

The dataset was generated using a technique for fast antenna design, which leveraged a machine learning framework with an infill criterion employing predicted enhancement of the merit function, utilizing a particle swarm optimizer as the primary search engine, and employing kriging for constructing the underlying surrogate model. The model operated within a reduced-dimensionality domain, guided by directions corresponding to maximum antenna response variability identified through fast global sensitivity analysis, tailored explicitly for domain determination. Operating within the reduced domain enabled building reliable surrogates at a significantly lower computational cost. To address the accuracy loss resulting from dimensionality reduction, the global optimization phase was supplemented by local sensitivity-based parameter adjustment.

Dataset file

metadata_p6_complete.pdf
408.8 kB, S3 ETag e7ce24fa03bfe929c74bc50726e72c6f-1, downloads: 0
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hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.

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https://github.com/antespi/s3md5

File details

License:
Creative Commons: by 4.0 open in new tab
CC BY
Attribution

Details

Year of publication:
2025
Verification date:
2025-03-17
Dataset language:
English
DOI:
DOI ID 10.34808/y20t-9s38 open in new tab
Funding:
Verified by:
Gdańsk University of Technology

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