Tuning the LC-Parameters of Metamaterial Unit Cells Using Genetic Algorithm

Ahmed, Sadiq; Chandra, Madhu; Al-Behadili, Husam

doi:10.1109/sims.2016.28

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Application field: Electromagnetics 159 Li, Chen, Zeng, et al [368] 2017 Adaptive GA optimization framework 160 Zhang and Cuii [369] 2017 BPSO optimization framework 161 Ahmed, Chandra, and Al-Behadili [370] 2017 GA Inverse design 162 Han, Cao, Gao, et al [371] 2017 GA optimization framework 163 Pfeiffer and Tomasic [372] 2017 GA optimization framework 164 Pelluri and Appasani [373] 2017 GA optimization framework 165 Feng, Chen, and Huang [374] 2017 GA optimization framework 166 Allen, Dykes, Reid, et al [375] 2017 GA optimization framework 167 Ding, Zhang, Zhang, et al [376] 2017 GA optimization framework 168 Mahdi and Taha [377] 2017 GA Topology optimization 169 Su, Lu, and Li [378] 2017 PSO optimization framework 170 Orlandi [379] 2018 Differential evolution optimization framework (DE) algorithm 171 Bagmancı, Karaaslan, Altıntaş, et al [380] 2018 GA optimization framework 172 Lim, Song, Kim, et al [381] 2018 GA optimization framework 173 Corrêa, Resende, Bicalho, et al [382] 2018 GA optimization framework 174 Kumar, Behera, and Suraj [383] 2018 GA optimization framework 175 Clemens, Iskander, Yun, et al [189] 2018 Hybrid genetic programming optimization framework 176 Soltani, Soltani, and Aguili [384] 2019 GA Inverse design 177 Ibili, Karaosmanoglu, and Ergul [385] 2019 GA optimization framework 178 Seshadri and Gupta [386] 2019 GA optimization framework 179 Nanda, De, Sahu, et al [387] 2019 GA optimization framework 180 Assal, Benzerga, Sharaiha, et al [388] 2019 GA optimization framework 181 Karatzidis, Kantartzis, Pyrialakos, et al [389] 2019 GA optimization framework 182 Tian and Li [390] 2019 GA optimization framework 183 Yuan, Ma, Sui, et al [391] 2019 GA Topology optimization 184 Yanzhang and Jinghao [392] 2019 GA Topology optimization 185 Sui, Ma, Chang, et al [393] 2019 IAGA optimization framework 186 Steckiewicz and Choroszucho [394] 2019 PSO optimization framework 187 Hao, Du, and Zhang …”

Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel metamaterials. The properties of metamaterials are essentially controllable via their architectures and until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially if the design space for metamaterial optimization is explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive metamaterial datasets can be used to very accurately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge. Here, we present a comprehensive review of the literature where state-of-the-art machine intelligence is used for the design, discovery and development of metamaterials. In this review, individual approaches are categorized based on methodology and application. We further present machine intelligence trends over a wide range of metamaterial design problems including: acoustics, photonics, plasmonics, mechanics, and more. Finally, we identify and discuss recent research directions and highlight current gaps in knowledge. KeywordsMetamaterials • Artificial intelligence • Machine learning • Neural Networks • Evolutionary algorithms • Surrogate modelling • Optimization 42 solely related to the properties of material constituents. 43 Properties can therefore be controlled and manipulated by 44 changing metamaterial 'unit cell' topology and while the 45 bulk properties of the constituent materials have influence, 46 these properties are often not the sole dominating variable 47 used in designing metamaterials. When designing with 48 respect to topology, the properties of metamaterials can 49 be customized within a very large design space, and this 50 is one of the reasons for the exponential growth in meta-51 materials R&D across the breadth of modern engineering.

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Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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“…This antenna displays superior radiation performance. The extracted LC parameters of the equivalent network using technique in [21] are shown in Table 1. Table 2 displays comparison bandwidth, efficiency, the reflection coefficients and gain, of the suggested antennas with other reference antenna.…”

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confidence: 99%

Design and bandwidth enhancement of zeroth-order resonator antenna using metamaterial

et al. 2022

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In this paper, a compact configuration of zeroth-order resonator antenna is described. The unit cell zeroth-order resonator properties are introduced by composite right/left-handed transmission line approach is fed by coplanar waveguide. The proposed resonator is analyzed by changing the coupling space and stub length of the unit cell. The size of implemented resonator is (0.185 λ0 × 0.185 λ0 × 0.027 λ0) at the centre frequency. In this work, a zeroth-order resonator antenna design with enhanced bandwidth has been presented, and the size reduction by using the metamaterial inclusion. The proposed zeroth-order resonant antenna (ZORA) achieves a 64% reduction compared to a traditional λ/2 microstrip patch antenna. The bandwidth for the 10 dB return loss is 5.66 GHz (2.76 GHz to 8.42 GHz), the peak value of gain is 0.8 dBi and radiation efficiency of the designed antenna is 87% at 5.5 GHz. The return loss is about -59.48 dB at the center frequency. The competition among the simulated performances and other antennas shows that the proposed resonator achieves wide bandwidth. The performance of zeroth-order resonator antenna is evaluated by full-wave electromagnetic (EM) simulator HFSS11.

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Metamaterial synthesis using a CAD model based on an evolutionary technique to improve the performance of TCAS antennas

Nanda

Sahu

et al. 2019

J Comput Electron

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Tuning the LC-Parameters of Metamaterial Unit Cells Using Genetic Algorithm

Cited by 4 publications

References 11 publications

Machine Intelligence in Metamaterials Design

Machine Intelligence in Metamaterials Design

Design and bandwidth enhancement of zeroth-order resonator antenna using metamaterial

Metamaterial synthesis using a CAD model based on an evolutionary technique to improve the performance of TCAS antennas

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