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Updated: Jan 25, 2026

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Hybrid machine learning and Gaussian process for antenna parameter estimation.

Hoang Thi Phuong Thao1, Tran Vu Kien2

  • 1Faculty of Electronics and Telecommunications, Electric Power University, Hanoi, Vietnam. thaohp@epu.edu.vn.

Scientific Reports
|January 23, 2026
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Summary
This summary is machine-generated.

This study introduces a hybrid machine learning (ML) model combining Random Forest (RF) and Gaussian Process (GP) for efficient microstrip patch antenna design across a wide frequency range, significantly reducing design time.

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Area of Science:

  • Electrical Engineering
  • Electromagnetics
  • Antenna Theory

Background:

  • Microstrip patch antennas are crucial components in modern wireless communication systems.
  • Efficient and accurate antenna design methods are essential for rapid development and deployment.
  • Existing machine learning (ML) approaches for antenna design often lack comprehensive validation and optimization.

Purpose of the Study:

  • To develop a hybrid ML model for accurate microstrip patch antenna design.
  • To optimize ML model hyper-parameters using Gaussian Process (GP) for superior predictive accuracy.
  • To validate the proposed model through extensive simulations and experimental verification.

Main Methods:

  • A hybrid model combining Random Forest (RF) and Gaussian Process (GP) was developed.
  • The model was trained and tested using a large dataset from CST full-wave simulations.
  • GP was utilized to optimize RF hyper-parameters, enhancing predictive performance.

Main Results:

  • The hybrid RF-GP model achieved a high predictive accuracy with a Root Mean Square Error (RMSE) of 0.0056.
  • The model demonstrated efficacy in designing microstrip patch antennas for frequencies ranging from 0.6 to 6.5 GHz.
  • Optimization time for antenna design was reduced by up to 99% compared to traditional methods.

Conclusions:

  • The proposed hybrid ML method offers a transformative approach to microstrip patch antenna design.
  • The model provides antenna designers with a tool for accurate designs across a broad frequency spectrum.
  • This approach significantly accelerates the antenna design process, improving efficiency and accuracy.