Computational Modeling of Electromagnetic Fields in Smart Magnetic Devices and Sensor Technologies

Abstract

Computational electromagnetic modeling has become an essential component in the design, optimization, calibration, and performance evaluation of smart magnetic devices and sensor technologies. Modern magnetic systems frequently contain nonlinear ferromagnetic materials, permanent magnets, conducting regions, miniature coils, magnetostrictive layers, piezoelectric components, and semiconductor sensing elements. Their behaviour is governed by strongly coupled electromagnetic, mechanical, thermal, and electronic phenomena that are difficult to describe through analytical equations alone. Numerical approaches such as the finite-element method, finite-difference time-domain method, boundary-element method, method of moments, and micromagnetic simulation enable researchers to calculate field distributions, magnetic forces, induced currents, energy losses, resonance behaviour, sensor sensitivity, and cross-axis interference before physical fabrication. This review examines the mathematical foundations, computational techniques, multiphysics coupling strategies, and engineering applications of electromagnetic-field modeling in smart magnetic devices. Particular attention is given to Hall-effect sensors, magnetoresistive sensors, magnetoelectric devices, fluxgate sensors, inductive sensors, microelectromechanical systems, magnetic actuators, and nanoscale spintronic components. The importance of geometry construction, material characterization, boundary conditions, mesh convergence, solver selection, model-order reduction, parameter optimization, and experimental validation is discussed. Emerging approaches involving digital twins, machine learning, physics-informed neural networks, uncertainty quantification, and high-performance computing are also evaluated. Computational models can significantly reduce prototype cycles and support device miniaturization, but their reliability depends on accurate material data, realistic operating conditions, systematic convergence analysis, and comparison with experimental measurements. Future smart magnetic systems will increasingly rely on hybrid physics-based and data-driven models capable of real-time prediction, adaptive calibration, and autonomous design optimization.