Machine-learning-based global optimization of microwave passives with variable-fidelity EM models and response features.

In practice, the process of rejecting (possibly significant) subset of the random samples acts as a pre-selection mechanism: it enables the identification of promising regions within the parameter space, concentrating the search process on these areas while excluding others.

also in 160 other papers