Machine-learning-assisted optimization and its application to antenna designs: Opportunities and challenges

Wu, Qi; Cao, Yi; Wang, Haiming; Wang, Hong

doi:10.23919/jcc.2020.04.014

Cited by 66 publications

(25 citation statements)

References 20 publications

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“…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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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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“…Therefore, the interest in its development, production, and optimization appeared through various simulation techniques. The methodology discussed in this paper can be categorized into three categories: designing the proposed antenna by combining two types of antennas, fabrication, and modeling by artificial intelligence [1].…”

Section: Introductionmentioning

confidence: 99%

“…Beside 3D electromagnetic simulation environment, the machine learning (ML) has been identified as competitive intelligence technique for antenna modeling and optimizing [1], and it can be widely utilized in several disciplines, such as engineering, education, science, meteorology, medicine, human resources recruiters, banking and economics. Various ML algorithms have been introduced to model characteristics of antennas, such as gain, directivity, and S-parameters ( 11 22, , .…”

Section: Introductionmentioning

confidence: 99%

“…Various ML algorithms have been introduced to model characteristics of antennas, such as gain, directivity, and S-parameters ( 11 22, , . S S etc ) [1]. They are mathematical processes performing random calculation during learning process and have the capability of learning and generalization to improve the antenna modeling and synthesis efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Design and Manufacture of a Multiband Rectangular Spiral-Shaped Microstrip Antenna Using EM-Driven and Machine Learning

Aoad

2021

ELEKTRON ELEKTROTECH

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This paper presents a multiband rectangular microstrip antenna using spiral-shaped configurations. The antenna has been designed by combining two configurations of microstrip and spiral with consideration of careful selection of the substrate material, the dimension of the rectangular microstrip, the distance between the turned spiral, and the number of turns of the spiral. The efficiency and accuracy have been improved using machine learning algorithms as well. Machine learning has been studied to model the proposed antenna based on the performance requirements, which requires a sufficient training data to improve the accuracy. Three different machine learning models are applied to improve the accuracy and generalization performance and compared to simulation and measurement results. Simulation, measurement, and machine learning results confirm that the proposed antenna is a new electrically small and operating over a wide range of high-frequency bands between 1 GHz–4 GHz. Machine learning models have the best prediction ability with a mean square error (MSE) of 0.03, and 0.05. The antenna structure and size are compatible and suitable for several multi-band wireless mobile systems operating in L-band and S-band. The results, such as directivity, Half-Power Beamwidth, Voltage Standing Wave Ratio (VSWR), and S-parameter curves, are analysed and compared with the numerical formulation for both spiral and microstrip antennas.

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“…However, the fidelity of the MITH is dependent on the sampling points in every iteration, and there is no guarantee that the real WCPs of the obtained MITH are found during the process, which leads to possible bias in the calculated MITH. Many surrogate model-based optimization methods have been introduced to address EM problems [9]- [16]. The key motivation of the surrogate model-based optimization algorithm is to use efficient surrogate models to accelerate the optimization procedure; these surrogate models can be built using physically coarse models or datadriven modeling strategies.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Machine Learning-Assisted Optimization-Based Robust Design and Its Applications to Antennas and Arrays

Chen¹,

Wu²,

Chen³

et al. 2020

Preprint

Self Cite

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An efficient multilayer machine learning-assisted optimization (ML-MLAO)-based robust design method is proposed for antenna and array applications. Machine learning methods are introduced into multiple layers of the robust design process, including worst-case analysis (WCA), maximum input tolerance hypervolume (MITH) searching, and robust optimization, considerably accelerating the whole robust design process. First, based on a surrogate model mapping between the design parameters and performance, WCA is performed using a genetic algorithm to ensure reliability. MITH searching is then carried out using a double-layer MLAO (DL-MLAO) framework to find the MITH of the given design point. Next, based on the training set obtained using DL-MLAO, correlations between the design parameters and the MITH are learned. The robust design is carried out using surrogate models for both the performance and the MITH, and these models are updated online following the ML-MLAO scheme. Furthermore, two examples, including an array synthesis problem and an antenna design problem, are used to verify the proposed ML-MLAO method. Finally, the numerical results and computation time are discussed to demonstrate the effectiveness of the proposed method.

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Machine-learning-assisted optimization and its application to antenna designs: Opportunities and challenges

Cited by 66 publications

References 20 publications

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

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

Design and Manufacture of a Multiband Rectangular Spiral-Shaped Microstrip Antenna Using EM-Driven and Machine Learning

Multilayer Machine Learning-Assisted Optimization-Based Robust Design and Its Applications to Antennas and Arrays

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