Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

The key challenge of expensive optimization problems (EOP) is that evaluating the true
fitness value of the solution is computationally expensive. A common method to deal with …

A digital twin can be defined as an adaptive model of a complex physical system. Recent
advances in computational pipelines, multiphysics solvers, artificial intelligence, big data …

This paper presents a literature review on methods for enabling real-time analysis in digital
twins, which are virtual models of physical systems. The advantages of digital twins are …

Design of contemporary antenna structures heavily relies on electromagnetic (EM)
simulations. Accurate reflection and radiation responses of many antenna geometries can …

Integrating data-driven surrogate models and simulation models of different accuracies (or
fidelities) in a single algorithm to address computationally expensive global optimization …

A computationally-efficient procedure for multi-objective design of antenna structures is
presented. Our approach exploits the multi-objective evolutionary algorithm (MOEA) working …

This communication presents a novel surrogate-assisted quasi-Newton enhanced global
optimization (SA-QNEGO) algorithm. In this proposed method, the heuristic hypersphere …

This book addresses computationally-efficient multi-objective optimization of antenna
structures using variable-fidelity electromagnetic simulations, surrogate modeling …

A methodology for fast multi-objective antenna optimization is presented. Our approach is
based on response surface approximation (RSA) modeling and variable-fidelity …