This paper presents a focused and comprehensive literature survey on the use of machine
learning (ML) in antenna design and optimization. An overview of the conventional …

An efficient, black-box multiobjective optimization technique is presented, which is capable
of simultaneously optimizing designs for performance as well as robustness when input …

A compact wideband dual-frequency microstrip antenna is proposed in this paper. By
employing an offset microstrip-fed line and a strip close to the radiating edges in the circular …

A two-stage approach based on Gaussian process regression that achieves significantly
reduced requirements for computationally expensive high-fidelity training data is presented …

In this paper, we present a multi-objective optimization technique for designing a patch
antenna on a finite ground plane by employing a surrogate model which is formulated by …

Gaussian process (GP) is a rapidly developing supervised machine learning (ML) method in
recent years, which has been widely used in the establishment of surrogate models in the …

A triple-band bow-tie-shaped CPW-fed slot antenna design for wireless communication
applications is proposed. The antenna consists of a signal strip, two conducting strips and …

When microstrip antennas (MSAs) are optimised, the full‐wave electromagnetic simulation
takes long time to get the result which is very time consuming. Hence, the knowledge‐based …

The applicability of Gaussian process (GP) regression as modeling tool within a genetic
algorithm (GA) antenna optimization scheme is demonstrated. GP regression has recently …

A compact dipole-type antenna fed by coplanar striplines (CPSs) with enhanced impedance
and gain performance across ultrawideband (UWB) operating bandwidth of 2.12–6 GHz is …