Prediction of Antenna Performance based on Scalable Data-informed Machine Learning Methods

Authors

  • Yiming Chen Department of Electrical Engineering Colorado School of Mines, Golden 80401, USA
  • Veysel Demir Department of Electrical Engineering Northern Illinois University, Dekalb 60115, USA
  • Srirama Bhupatiraju Antenna Group Nvidia, Santa Clara 95050, USA
  • Atef Z. Elsherbeni Department of Electrical Engineering Colorado School of Mines, Golden 80401, USA
  • Joselito Gavilan Antenna Group Nvidia, Santa Clara 95050, USA
  • Kiril Stoynov Antenna Group Nvidia, Santa Clara 95050, USA

DOI:

https://doi.org/10.13052/2024.ACES.J.390401

Keywords:

Data informed, ensemble, full-wave simulation, machine learning, scalability, stacking, wide frequency range

Abstract

This paper proposes a scalable architecture for predicting antenna performance using various data-informed machine learning (DIML) methods. By utilizing the computation power of graphics processing units (GPUs), the architecture takes advantage of hardware (HW) acceleration from the beginning of electromagnetic (EM) full-wave simulation to the final machine learning (ML) validation. A total of 49152 full-wave simulations of a classical microwave patch antenna forms the ML dataset. The dataset contains the performance of patch antenna on six commonly used materials and two standard thicknesses in a wide frequency range from 0.1 to 20 GHz. A total of 13 base ML models are stacked and ensembled in a tabular workflow with performance as 0.970 and 0.933 F1 scores for two classification models, as well as 0.912 and 0.819 R2 scores for two regression models. Moreover, an image-based workflow is proposed. The image-based workflow yields the 0.823 R2 score, indicating a near real-time prediction for all S11 values from 0.1 to 20 GHz. The proposed architecture requires neither the fine-tuned hyperparameters in the ML-assisted optimization (MLAO) model for specified antenna design nor the pre-knowledge required in the physics-informed models. The fully automated process with data collection and the customized ML pipeline provides the architecture with robust scalability in future work where more antenna types, materials, and performance requirements can be involved. Also, it could be wrapped as a pre-trained ML model as a reference for other antenna designs.

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Author Biographies

Yiming Chen, Department of Electrical Engineering Colorado School of Mines, Golden 80401, USA

Yiming Chen received the M.S. degree in Electrical Engineering from the China University of Mining and Technology, Xuzhou, China, in 2019. Currently, he is a Ph.D. candidate from the ARC group at the Department of Electrical Engineering, Colorado School of Mines, Colorado, USA. His interests are in antenna design with machine learning, metasurface array optimization, on-body antenna, and RFID.

Veysel Demir, Department of Electrical Engineering Northern Illinois University, Dekalb 60115, USA

Veysel Demir is an Associate Professor at the Department of Electrical Engineering at Northern Illinois University, USA. He received his Bachelor of Science degree in Electrical Engineering from Middle East Technical University, Ankara, Turkey, in 1997. He studied at Syracuse University, New York, where he received both a Master of Science and Doctor of Philosophy degrees in Electrical Engineering in 2002 and 2004, respectively. During his graduate studies, he worked as a Research Assistant for Sonnet Software, Inc., Liverpool, New York. He worked as a visiting Research Scholar in the Department of Electrical Engineering at the University of Mississippi from 2004 to 2007. He joined Northern Illinois University in August 2007 and served as an Assistant Professor until August 2014. He has been serving as an Associate Professor since then.

Srirama Bhupatiraju, Antenna Group Nvidia, Santa Clara 95050, USA

Srirama Bhupatiraju received his master’s degree in telecommunication engineering from the University of Texas, Dallas, USA, in 2012. He is a Senior Antenna Engineer with the Wireless and Radio Group at Nvidia, Santa Clara, USA. He has 10+ years of experience in the consumer electronics industry and his areas of interests are high performance computing, Desense, EMI, Antenna, and RF system design.

Atef Z. Elsherbeni, Department of Electrical Engineering Colorado School of Mines, Golden 80401, USA

Atef Z. Elsherbeni received an honor B.Sc. degree in Electronics and Communications, an honor B.Sc. degree in Applied Physics, and a M.Eng. degree in Electrical Engineering, all from Cairo University, Cairo, Egypt, in 1976, 1979, and 1982, respectively, and a Ph.D. degree in Electrical Engineering from Manitoba University, Winnipeg, Manitoba, Canada, in 1987. He started his engineering career as a part time Software and System Design Engineer from March 1980 to December 1982 at the Automated Data System Center, Cairo, Egypt. From January to August 1987, he was a Post-Doctoral Fellow at Manitoba University. Dr. Elsherbeni joined the faculty at the University of Mississippi in August 1987 as an Assistant Professor of Electrical Engineering. He advanced to the rank of Associate Professor in July 1991, and to the rank of Professor in July 1997. He was the Associate Dean of the College of Engineering for Research and Graduate Programs from July 2009 to July 2013 at the University of Mississippi. He then joined the Electrical Engineering and Computer Science (EECS) Department at Colorado School of Mines in August 2013 as the Dobelman Distinguished Chair Professor. He was appointed the Interim Department Head for EECS from 2015 to 2016 and from 2016 to 2018 he was the Electrical Engineering Department Head. He spent a sabbatical term in 1996 at the Electrical Engineering Department, University of California at Los Angeles (UCLA) and was a visiting Professor at Magdeburg University during the summer of 2005 and at Tampere University of Technology in Finland during the summer of 2007. In 2009 he was selected as Finland Distinguished Professor by the Academy of Finland and TEKES. Dr. Elsherbeni is an IEEE Life Fellow and ACES Fellow. He is the Editor-in-Chief for ACES Journal, and a past Associate Editor to the Radio Science Journal. He was the Chair of the Engineering and Physics Division of the Mississippi Academy of Science, the Chair of the Educational Activity Committee for IEEE Region 3 Section, and the general Chair for the 2014 APS-URSI Symposium and the President of ACES Society from 2013 to 2015. Dr. Elsherbeni is selected as Distinguished Lecturer for IEEE Antennas and Propagation Society for 2020-2023. He is the recent recipient of the 2023 IEEE APS Harington-Mittra Award for his contribution to computational electromagnetics with hardware acceleration.

Joselito Gavilan, Antenna Group Nvidia, Santa Clara 95050, USA

Joselito Gavilan holds a B.S. degree in Electrical Engineering and a master’s in engineering degree with a focus on Electromagnetics and Wireless Communication, both earned from the University of Illinois at Chicago in ’98 and ’04 respectively. Joselito has over 20 years of product design experience, ranging from base station antenna products to mobile phones, tablets, laptops, gaming devices, and high-performance edge computers. His interest is product integration and simulation. He enjoys the challenges of tight integration and learning the constraints of cross-functional disciplines to make the right tradeoffs. Currently, he is at NVIDIA, leading efforts to reimagine computational simulations given the accessibility of high-performance supercomputers and AI technologies.

Kiril Stoynov, Antenna Group Nvidia, Santa Clara 95050, USA

Kiril Stoynov received the M.S. degree in Electrical Engineering with concentration in Computational Electromagnetics from the University of Akron in 2008. Currently he is an EPM in the Systems Products Team at Nvidia working on Industrial and Embedded Products. Kiril has interests in antenna design, simulations, HPC, AI and simulations on a large scale.

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Published

2024-04-30

How to Cite

[1]
Y. Chen, V. Demir, S. Bhupatiraju, A. Z. Elsherbeni, J. Gavilan, and K. Stoynov, “Prediction of Antenna Performance based on Scalable Data-informed Machine Learning Methods”, ACES Journal, vol. 39, no. 04, pp. 275–290, Apr. 2024.

Issue

2024: Vol 39 Iss 4

Section

General Submission

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