This article presents an overview of artificial neural network (ANN) techniques for a
microwave computer-aided design (CAD). ANN-based techniques are becoming useful for …

In this article, a two‐port multiple‐input multiple‐output (MIMO) hybrid rectangular dielectric
resonator antenna (DRA) with machine learning (ML) approach for the n261 5G New Radio …

In this article, we have presented the use of prior physics knowledge-based artificial neural
networks (KBANNs) to improve the simulation speed and accuracy of compact models for …

In this article, machine learning (ML) metamodels have been developed in order to predict
the per-unit-length parameters of hybrid copper–graphene on-chip interconnects based on …

Thanks to their convex formulation, kernel regressions have shown an improved accuracy
with respect to artificial neural network (ANN) structures in regression problems where a …

This paper deals with the development of a Machine Learning (ML)-based regression for the
construction of complex-valued surrogate models for the analysis of the frequency-domain …

In this article, a knowledge-based artificial neural network (ANN) is developed for predicting
jitter in CMOS inverter circuits in the presence of power supply noise (PSN). The proposed …

Multifunctional frequency-selective surfaces (FSSs) provide many possibilities for controlling
electromagnetic waves as well as for reducing electromagnetic interferences (EMIs) …

on-chip interconnects are very important in both integrated circuits and systems, affecting
signal transmission directly. To design the interconnects with better performance, designers …

In this article, a modified knowledge-based artificial neural network (KBANN) metamodel is
developed for the efficient uncertainty quantification of on-chip multiwalled carbon nanotube …