<scp>Variable‐fidelity</scp> modeling of antenna input characteristics using domain confinement and <scp>two‐stage</scp> Gaussian process regression surrogates

Jacobs, J. Pieter; Koziel, Slawomir

doi:10.1002/jnm.2758

Cited by 3 publications

(1 citation statement)

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“…A multistage SBO technique with data mining and a local search mechanism is suggested in [84] based on the algorithmic framework in [87] to reliably address model differences in multi-fidelity antenna optimization while assuring high efficiency and better convergence speed. In [88], a new VFO method is proposed to model the input characteristics of an antenna based on domain confinement and two-stage GPR surrogates. The high CPU cost of the extensive simulations required by parametric optimization, uncertainty quantification, or robust design processes is the primary constraint in EM-driven antenna design.…”

Section: Variable-fidelity Optimizationmentioning

confidence: 99%

Applications of Machine Learning and Deep Learning in Antenna Design, Optimization, and Selection: A Review

Sarker,

Podder,

Mondal

et al. 2023

IEEE Access

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This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process.

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Section: Variable-fidelity Optimizationmentioning

confidence: 99%

Applications of Machine Learning and Deep Learning in Antenna Design, Optimization, and Selection: A Review

Sarker,

Podder,

Mondal

et al. 2023

IEEE Access

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Locating Pareto optimal designs of antenna through parallel multiobjective evolutionary algorithm

Ding

Zhang

et al. 2021

Int J RF Microw Comput Aided Eng

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Population‐based multiobjective metaheuristics are potential in microwave component designs because all electric properties could be optimized simultaneously to find several tradeoff designs in a single run. However, they often suffer extremely‐expensive computational time caused by too many full‐time objective function evaluations. In this paper, firstly, an efficient parallel implementation of MOEA/D (multiobjective evolutionary algorithm based on decomposition) is proposed to reduce the overall computational time. Secondly, optimization speed, approximation performance to the entire Pareto front, and algorithm robustness of the proposed parallel MOEA/D are investigated through extensive standard test instances. Then, a complete design procedure based on the proposed optimization algorithm is presented to locate multiple Pareto optimal designs of antenna. Finally, a compact broadband dual‐polarized antenna operating at 3.3‐5.0 GHz is designed for 5G base station applications. Comparisons are made to verify the great performance of the proposed method.

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Efficient modelling of compact microstrip antenna using machine learning

Sharma

Pandey

2021

AEU - International Journal of Electronics and Communications

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Variable‐fidelity modeling of antenna input characteristics using domain confinement and two‐stage Gaussian process regression surrogates

Cited by 3 publications

References 38 publications

Applications of Machine Learning and Deep Learning in Antenna Design, Optimization, and Selection: A Review

Applications of Machine Learning and Deep Learning in Antenna Design, Optimization, and Selection: A Review

Locating Pareto optimal designs of antenna through parallel multiobjective evolutionary algorithm

Efficient modelling of compact microstrip antenna using machine learning

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