Numerical and experimental generated data during project https://doi.org/10.1038/s41598-024-77367-w

The dataset was generated using a two-stage methodology for global design optimization of antenna systems. Its keystone components included dimensionality reduction realized by means of fast global sensitivity analysis (FGSA), a machine learning (ML) procedure involving kriging surrogate models, and fine-tuning of antenna parameters using accelerated trust-region (TR) search.
Extensive verification experiments involved four antennas of diverse responses (multi-band, broadband, enhanced gain). The results demonstrated consistent operation, reliability, repeatability of solutions, and excellent cost efficiency of the presented framework. The average running cost of the algorithm corresponded to only about 140 EM antenna simulations, which is comparable to the expenses incurred by local optimization.