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In present scenario, in transformation of electromagnetic (EM) simulation technology, the Machine Learning (ML) techniques hold significant potential. Because of present computational constraints of electromagnetic tools and modeling 3D structures with numerous design parameters, optimization of antenna is a challenging and resource-intensive process. The integration of Machine Learning techniques into simulation tools offers a promising solution to this issue. Machine learning algorithms have proven to be highly effective for the estimation of parameters in antenna design. In this paper, a few of such ML algorithms will be discussed, which are being implemented by researchers to compute the final optimized dimensions of the antenna designs.

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Exploring Machine Learning in Microstrip Patch Antenna Optimization

  • 章节

  • 发表日期：

    2026年

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  • Alka Verma,
  • Shilpee Patil,
  • Anil Kumar Pandey,
  • Nabeel Al-Milli,
  • Arunkumar Rana,
  • Jayant Giri

摘要

In present scenario, in transformation of electromagnetic (EM) simulation technology, the Machine Learning (ML) techniques hold significant potential. Because of present computational constraints of electromagnetic tools and modeling 3D structures with numerous design parameters, optimization of antenna is a challenging and resource-intensive process. The integration of Machine Learning techniques into simulation tools offers a promising solution to this issue. Machine learning algorithms have proven to be highly effective for the estimation of parameters in antenna design. In this paper, a few of such ML algorithms will be discussed, which are being implemented by researchers to compute the final optimized dimensions of the antenna designs.