Machine-Learning Approach for Design of Nanomagnetic-Based Antennas Article

Gianfagna, C, Yu, H, Swaminathan, M et al. (2017). Machine-Learning Approach for Design of Nanomagnetic-Based Antennas . JOURNAL OF ELECTRONIC MATERIALS, 46(8), 4963-4975. 10.1007/s11664-017-5487-8

cited authors

  • Gianfagna, C; Yu, H; Swaminathan, M; Pulugurtha, R; Tummala, R; Antonini, G

abstract

  • We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.

publication date

  • August 1, 2017

published in

Digital Object Identifier (DOI)

start page

  • 4963

end page

  • 4975

volume

  • 46

issue

  • 8