Antenna Design Exploration and Optimization using Machine Learning

Maeurer, Christoph; Futter, Peter; Gampala, Gopinath

doi:10.23919/eucap48036.2020.9135530

Cited by 27 publications

(8 citation statements)

References 1 publication

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“…This paper presents a novel machine learning (ML) based approach to ease the selection of the MM for different cases. Due to the rapid growth and diversity of available data, ML has become a technology that is being frequently used to find an optimized solution to a given problem in a wide variety of applications, including electromagnetics and antennas [29], [30]. ML's popularity in concepts related to electromagnetics is rooted in its ability to improve the computational cost when solving complex electromagnetic problems with a data-driven approach [31].…”

Section: Accepted Manuscript / Clean Copymentioning

confidence: 99%

Machine Learning-Based Matching Medium Design for Implant Communications

Cil

Cadir²,

Kati³

et al. 2022

IEEE Trans. Antennas Propagat.

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Matching media are used in various applications to increase the power transmitted into the human body. The selection of the optimum matching medium permittivity is not a straightforward task, as the optimum value maximizing the transmitted power depends on the thickness of the matching medium and the electromagnetic properties of the target tissue. In this paper, a computationally heavy empirical approach and a machine learning-based approach are utilized for the selection of the matching medium. The empirical approach demonstrates that the matching medium can increase the |S 21 | values up to 8 dB, which is validated with measurements. Next, a machine learning-based tool is proposed to predict the optimum matching medium permittivity for any target tissue and any matching medium thickness. A one-dimensional convolutional neural network followed by a multi-layer perceptron is trained with the simulated average Poynting vector magnitudes for muscle and fat as target tissues. The average Poynting vector magnitude and the dipole length for given system parameters are predicted by the trained artificial neural network. The accuracy is calculated by comparison with the results of the empirical analysis and found to be 1% and 12.3% mean absolute percentage error for dipole length and average Poynting vector magnitude, respectively. The proposed tool decreases the time required to milliseconds.

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Section: Accepted Manuscript / Clean Copymentioning

confidence: 99%

Machine Learning-Based Matching Medium Design for Implant Communications

Cil

Cadir²,

Kati³

et al. 2022

IEEE Trans. Antennas Propagat.

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show abstract

“…Overall, there is more research carried out on machine learning for improving the runtime of the optimization of electromagnetic devices, as was also shown in Table 2. Different machine learning algorithms, such as SVM, multi-layer perceptron (MLP), Knearest neighbor (KNN), and CNN have been investigated to optimize transformers, antennas, and motors (motors are the majority applications) [135][136][137][138][139][140][141][142][146][147][148][149][150]. It is noted that deep learning follows promising results when applied for topology optimization of electromagnetic devices, and this topic has attracted much attention recently [143][144][145].…”

Section: Machine Learning For Optimization Of Electromagnetic Devicesmentioning

confidence: 99%

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

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This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

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“…ML has been used in different applications of antenna design and optimization [37][38][39]. Lecci et al [40] proposed an ML framework that enabled a simulation-based optimization of thinned arrays, considering network-level metrics such as signal to interference plus noise ratio statistics, based on a Monte Carlo approach.…”

Section: Design Optimization and Synthesismentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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Antenna Design Exploration and Optimization using Machine Learning

Cited by 27 publications

References 1 publication

Machine Learning-Based Matching Medium Design for Implant Communications

Machine Learning-Based Matching Medium Design for Implant Communications

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Application of Machine Learning in Electromagnetics: Mini-Review

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