ANN based optimization of resonating frequency of split ring resonator

Sarmah, Kumaresh; Sarma, Kandarpa Kumar; Baruah, Sanjoy

doi:10.1109/cicomms.2014.7014633

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…With time, it will be integrated into all levels of technology readiness, becoming commonplace as an assistive tool in the computational design of metamaterials. Weng, Ding, Hu, et al [266] 2020 Deep Learning Classification 10 Melo Filho, Angeli, Ophem, et al [267] 2020 ANN Inverse Design 11 Chen, Lu, Karniadakis, et al [268] 2020 Deep Learning Inverse Design 12 Kollmann, Abueidda, Koric, et al [269] 2020 Deep Learning Optimization Framework 13 Qu, Zhu, Shen, et al [270] 2020 ANN Optimization Framework 14 Lai, Amirkulova, and Gerstoft [271] 2021 CNN, GAN Inverse Design 15 Gurbuz, Kronowetter, Dietz, et al [272] 2021 GAN Inverse Design 16 Amirkulova, Tran, and Khatami [273] 2021 Deep Learning Inverse Design 17 Wu, Liu, Jahanshahi, et al [274] 2021 ANN Inverse Design 18 Shah, Zhuo, Lai, et al [275] 2021 RL Optimization Framework 19 Tran, Amirkulova, and Khatami [276] 2022 ANN Inverse Design 20 Wiest, Seepersad, and Haberman [277] 2022 GNN Inverse Design 21 Amirkulova, Zhou, Abbas, et al [278] 2022 Deep Learning Inverse Design 22 Tran, Khatami, and Amirkulova [279] 2022 CNN Inverse Design 23 Li, Chen, Li, et al [280] 2023 CNN Inverse Design 24 Li, Chen, Li, et al [281] 2023 Deep Learning Inverse Design 25 Wang, Chen, Xu, et al [282] 2023 ANN Inverse Design Application field: Electromagnetics 26 Jiang, Xiao, Liu, et al [283] 2010 ANN and scaled conjugate Surrogate model gradient 27 Freitas, Rêgo, and Vasconcelos [140] 2011 ANN Surrogate model 28 Vasconcelos, Rêgo, and Cruz [139] 2012 ANN Surrogate model 29 Sarmah, Sarma, and Baruah [172] 2015 ANN optimization framework 30 Saha and Maity [138] 2016 ANN Surrogate model 31 Nanda, Sahu, and Mishra [108] 2019 ANN Inverse design 32 An, Fowler, Shalaginov, et al [144] 2019 ANN Surrogate model 33 Yuze, Hai, and Qinglin [157] 2019 CNN Classification and clustering 34 Liu, Zhang, and Cui [156] 2019 CNN optimization framework 35 Hodge, Mishra, and Zaghloul [284] 2019 DC-GAN Inverse design 36 Hodge, Mishra, and Zaghloul…”

Section: The Causal Relationship Problemmentioning

confidence: 99%

“…In their findings, they note that although this 1442 gradient-based method might still get caught at a local 1443 minima, it is much faster than conventional numerical 1444 gradient-based approaches, such as finite-difference meth-1445 ods (FDM). Sarmah et al[172] proposed the use of an 1446 ANN-based optimization framework, trained using several 1447 gradient descent algorithms. Their findings suggest that 1448 ANN is highly suitable for the optimization of design pa-1449 rameters and in their case for defining the performance of 1450[174] discusses the 1466 optimization of ANN architecture and model parameters 1467 in more detail.…”

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confidence: 99%

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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: The Causal Relationship Problemmentioning

confidence: 99%

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confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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“…ANN are machine-learning tools that are very effective when we have data, which is experimentally generated. ANN are used where we have no mathematical formula to calculate the output for a certain input and there is no linear relationship between the input and output parameters, so the ANN's are the best tool for the prediction and for the Optimization [3][4][5] of output parameters. In this kind of scenario, ANN reduces the complex calculations [6-8] and reduces the computational cost.…”

Section: Introductionmentioning

confidence: 99%

Metamaterial Parameter Estimation by Machine Learning Method

Tiwari,

Sharma,

Ali

2024

Preprint

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Artificial neural network modeling is used to synthesize the metamaterial unit cell. Artificial neural networks are powerful tools to establish the relation between inputs and outputs parameters under highly nonlinear conditions. Artificial neural networks captured the synaptic weights according to their training data set. In artificial neural networks, the back propagation technique is the fastest learning method, which reduces the computer’s processing time and provides the best results under the nonlinear relationship between input and output. This work is divided into three parts. In the first part, we design a metamaterial unit cell, which is in the shape of square split rings. This shape is widely used to realize a metamaterial unit cell. In the second part, we develop a regression model using artificial neural networks to estimate the output resonance frequency when design parameters are used as input of artificial neural networks. In the last part, we use three different machine learning method to estimate the output parameter and then do the comparison in between them. Therefore, the objective of this research work is to develop a hypothesis using feed forward backpropagation method, Bayesian regularization and Elman backpropagation method, to find the resonance frequency when dimension of the metamaterial unit cell is given.

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