The Prediction of Chiral Metamaterial Resonance using Convolutional Neural Networks and Conventional Machine Learning Algorithms

Abstract

Electromagnetic resonance is the most important distinguishing property of metamaterials to examine many unusual phenomena. The resonant response of metamaterials can depend many parameters such as geometry, incident wave polarization. The estimation and the design of the unit cells can be challenging for the required application. The research on resonant behavior can yield promising applications. We investigate the resonance frequency of the chiral resonator as a unit of chiral metamaterial employing both traditional machine learning algorithms and convolutional deep neural networks. To our knowledge, this is the very first attempt on chiral metamaterials in that comparing the impact of various machine learning algorithms and deep learning model. The effect of geometrical parameters of the chiral resonator on the resonance frequency is studied. For this purpose, convolutional neural networks, support vector machines, naive Bayes, decision trees, random forests are employed for classification of resonance frequency. Extensive experiments are performed by varying training set percentages, epoch sizes, and data sets. Experiment results demonstrate that the usage of convolutional neural networks is superior in terms of prediction performance of chiral metamaterial resonance compared to the other techniques with 58.29% of accuracy on dataset1, and 68.77% of accuracy on dataset2.

Examining the HAP+ Fe2O3 Response in the Magnetic Field, Radiation Protection and Mechanical Properties

“…Thus besides other code it has been in a variety of studies in radiation fields [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. In the present study, the FLUKA Monte Carlo Code is utilized to calculate the radiation shielding capacity of the HAP+ Fe2O3 samples [18,[40][41].…”

Harnessing Fe 2 O 3 to Improve HAP Composites: Investigating Radiation Shielding, Mechanical Attributes, and Magnetic Field Effects

“…FLUKA is an important and widely used Monte Carlo code for different applications [20]. Thus besides other code it has been in a variety of studies in radiation elds [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. In the present study, the FLUKA Monte Carlo Code is utilized to calculate the radiation shielding capacity of the HAP + Fe 2 O 3 samples [18, [40][41].…”

Assessment of Gamma Ray Shielding Properties for Skin

“…The conventional shielding materials for several years are lead, tungsten, and other heavy elements. Besides these materials which have a high absorption rate, researcher developed new alternative materials due to negative aspect of this conventional materials [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In order to obtain cell from harmful radiation effect, the shielding character of the skin itself should be known.…”

“…Roya Boodaghi MALIDARRE, Huseyin Ozan TEKIN, Kadir GUNOGLU, Hakan AKYILDIRIM/ IJCESEN 9-1(2023)[6][7][8][9][10] …”