Numerical and experimental generated data during project https://doi.org/10.1038/s41598-025-25969-3

The dataset was generated during a project aimed at developing a procedure for low-cost globalized optimization of microwave circuits. Its keystones are simplexbased regression surrogates constructed to represent the circuit’s operating parameters. Geometrical simplicity of the surrogate and only a slightly nonlinear relation between the circuit dimensions and operating parameters, as well as conducting global search using low-resolution EM simulations, lead to a remarkable cost efficiency of the algorithm. Meanwhile, the assumed simplex updating rules guarantee convergence. The reliability is secured by a supplementary fine tuning executed using highresolution EM models. As demonstrated, the presented framework exhibits perfect success rate with satisfactory designs found in each algorithm run out of multiple instances executed. The cost is just sixty high-resolution EM analyses, whereas design quality is competitive over the benchmark methods.