Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO Antennas
Machine learning (ML) has gained recognition as an efficient and robust technique to realize the solution of electromagnetic forward and inverse problems. This article introduces a hybrid ML framework that simultaneously acts as a forward and inverse model based on a mode input. Multivariate relevan...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10052644/ |
| _version_ | 1811159110180143104 |
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| author | Debanjali Sarkar Taimoor Khan Jayadeva Ahmed A. Kishk |
| author_facet | Debanjali Sarkar Taimoor Khan Jayadeva Ahmed A. Kishk |
| author_sort | Debanjali Sarkar |
| collection | DOAJ |
| description | Machine learning (ML) has gained recognition as an efficient and robust technique to realize the solution of electromagnetic forward and inverse problems. This article introduces a hybrid ML framework that simultaneously acts as a forward and inverse model based on a mode input. Multivariate relevance vector regression (MVRVR) is adopted for implementing the hybrid ML model. MVRVR models for forward and inverse modeling are also presented. In addition, three hybrid ML models based on support vector regression (SVR), Gaussian process regression (GPR), and artificial neural network (ANN) are also implemented and a thorough comparative analysis between these ML models with the proposed MVRVR model is investigated to verify its accuracy. The proposed hybrid framework can be used to replace the requirements of the two separate models for solving forward and inverse problems. Two examples of ultra-wideband (UWB) MIMO antennas are employed to validate the effectiveness of the proposed modeling framework. |
| first_indexed | 2024-04-10T05:35:29Z |
| format | Article |
| id | doaj.art-4dd9aac47ee94ece9a57aa2137f3824c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T05:35:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4dd9aac47ee94ece9a57aa2137f3824c2023-03-07T00:00:40ZengIEEEIEEE Access2169-35362023-01-0111196451965610.1109/ACCESS.2023.324896110052644Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO AntennasDebanjali Sarkar0https://orcid.org/0000-0002-8509-3052Taimoor Khan1https://orcid.org/0000-0002-3960-1423 Jayadeva2Ahmed A. Kishk3https://orcid.org/0000-0001-9265-7269School of Electronics Engineering, VIT-AP University, Amaravati, IndiaDepartment of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, IndiaDepartment of Electrical and Computer Engineering, Concordia University, Montreal, CanadaMachine learning (ML) has gained recognition as an efficient and robust technique to realize the solution of electromagnetic forward and inverse problems. This article introduces a hybrid ML framework that simultaneously acts as a forward and inverse model based on a mode input. Multivariate relevance vector regression (MVRVR) is adopted for implementing the hybrid ML model. MVRVR models for forward and inverse modeling are also presented. In addition, three hybrid ML models based on support vector regression (SVR), Gaussian process regression (GPR), and artificial neural network (ANN) are also implemented and a thorough comparative analysis between these ML models with the proposed MVRVR model is investigated to verify its accuracy. The proposed hybrid framework can be used to replace the requirements of the two separate models for solving forward and inverse problems. Two examples of ultra-wideband (UWB) MIMO antennas are employed to validate the effectiveness of the proposed modeling framework.https://ieeexplore.ieee.org/document/10052644/Machine learningMIMO antennamultivariate relevance vector regression (MVRVR)ultra-wideband (UWB) |
| spellingShingle | Debanjali Sarkar Taimoor Khan Jayadeva Ahmed A. Kishk Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO Antennas IEEE Access Machine learning MIMO antenna multivariate relevance vector regression (MVRVR) ultra-wideband (UWB) |
| title | Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO Antennas |
| title_full | Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO Antennas |
| title_fullStr | Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO Antennas |
| title_full_unstemmed | Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO Antennas |
| title_short | Machine Learning Assisted Hybrid Electromagnetic Modeling Framework and Its Applications to UWB MIMO Antennas |
| title_sort | machine learning assisted hybrid electromagnetic modeling framework and its applications to uwb mimo antennas |
| topic | Machine learning MIMO antenna multivariate relevance vector regression (MVRVR) ultra-wideband (UWB) |
| url | https://ieeexplore.ieee.org/document/10052644/ |
| work_keys_str_mv | AT debanjalisarkar machinelearningassistedhybridelectromagneticmodelingframeworkanditsapplicationstouwbmimoantennas AT taimoorkhan machinelearningassistedhybridelectromagneticmodelingframeworkanditsapplicationstouwbmimoantennas AT jayadeva machinelearningassistedhybridelectromagneticmodelingframeworkanditsapplicationstouwbmimoantennas AT ahmedakishk machinelearningassistedhybridelectromagneticmodelingframeworkanditsapplicationstouwbmimoantennas |