Utilizing machine learning techniques for predictive modelling of absorptivity in l-shaped metamaterials

Utilizing machine learning techniques for predictive modelling of absorptivity in l-shaped metamaterials

Md Adil, Pratik Punj

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Abstract. Metamaterials are artificially engineered materials that have properties not found in naturally occurring materials. They are designed to have specific electromagnetic or other physical properties, such as negative refraction, superconductivity or high absorptivity. They are often composed of structures on a scale much smaller than the wavelength of the phenomena they are intended to manipulate. Metamaterials have a wide range of potential applications, including in antennas, cloaking devices, and super resolution imaging. In this paper we have simulated and validated an L shaped meta material to make a data set of its absorptivity by varying different input parameters and then used these data to predict the absorptivity of any L shaped metamaterial using machine learning and it gave satisfactory results.

Metamaterials, Machine Learning, Absorptivity, Simulation, Predictive Modelling

Published online 8/10/2023, 10 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Md Adil, Pratik Punj, Utilizing machine learning techniques for predictive modelling of absorptivity in l-shaped metamaterials, Materials Research Proceedings, Vol. 31, pp 656-665, 2023

DOI: https://doi.org/10.21741/9781644902592-67

The article was published as article 67 of the book Advanced Topics in Mechanics of Materials, Structures and Construction

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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