Mr. Pawan Gaire | Electromagnetic | Best Researcher Award

Graduate Research Assistant at University of Nebraska Lincoln, United States

Pawan K. Gaire is a Ph.D. candidate in Electrical Engineering at the University of Nebraska-Lincoln, with expertise in electromagnetic (EM) simulation, numerical modeling, and RF/antenna design. His research focuses on developing novel computational techniques and advanced RF systems for wireless communication and energy transfer. With a strong background in both theoretical and applied electromagnetics, he has contributed to groundbreaking advancements in physics-embedded neural networks, multi-band antennas, and wireless power transfer technologies.

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Education Background

Pawan earned his B.S. in Electrical Engineering from Howard University (summa cum laude, GPA: 3.89) in 2019. He then pursued graduate studies at Florida International University (GPA: 3.96) from 2019 to 2022 before transferring to the University of Nebraska-Lincoln, where he is currently completing his Ph.D. (GPA: 4.0), expected in May 2025. His coursework includes advanced topics in RF circuit design, antenna and wireless communication systems, numerical analysis, and electromagnetic modeling.

Professional Development

Pawan has extensive research experience as a Research Assistant at the University of Nebraska-Lincoln, where he developed the Physics Embedded Neural Network (PENN) for solving finite element problems and designed miniature multi-band antennas using multiferroic heterostructures. His work also includes the simulation of vector vortex wave generation for high-capacity wireless communication in confined environments. Previously, at Florida International University, he designed and implemented wireless power transfer systems, including smartphone charging within the radiating near-field and rectifier circuits for wearable energy harvesting. His industry experience includes internships at SLAC National Accelerator Laboratory, DiCarlo Lab at MIT’s Center for Brain, Mind, and Machines, and the Center for Integrated Quantum Materials, where he worked on projects involving photolithography, quantum materials, and AI-driven computer vision benchmarking.

Research Focus

Pawan’s research interests lie at the intersection of computational electromagnetics, physics-informed machine learning, RF and microwave systems, wireless power transfer, and antenna design. He is particularly focused on leveraging AI-driven approaches to solve Maxwell’s equations efficiently and designing next-generation RF systems for advanced communication and energy harvesting applications.

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Publication Top Notes

1. Physics Embedded Neural Network: Novel Data-Free Approach Towards Scientific Computing and Applications in Transfer Learning

• Authors: P. Gaire, S. Bhardwaj

• Journal: Neurocomputing, Volume 617, Article 128936

• Year: 2025

• Paper Summary: This paper introduces Physics Embedded Neural Networks (PENN), a novel approach to solving partial differential equations (PDEs) without relying on large datasets. The study demonstrates its effectiveness in scientific computing, particularly for Maxwell’s equations, and explores its applications in transfer learning.

2. Physics Embedded Neural Network (PENN) Architecture for Solving Maxwell’s Equations Towards Accelerated Microwave Modeling

• Authors: P. Gaire, S. Bhardwaj

• Conference: 2023 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON)

• Pages: 1-5

• Year: 2023

• Paper Summary: This paper presents an optimized neural network-based framework for solving Maxwell’s equations, significantly reducing computation time in microwave modeling. The PENN model is benchmarked against traditional finite element method (FEM) solvers, demonstrating superior efficiency in electromagnetic simulations.

3. An Ergonomic Wireless Charging System for Integration with Daily Life Activities

• Authors: D. Vital, P. Gaire, S. Bhardwaj, J.L. Volakis

• Journal: IEEE Transactions on Microwave Theory and Techniques, Volume 69, Issue 1, Pages 947-954

• Year: 2020

• Citations: 26

• Paper Summary: This study proposes a wireless charging system seamlessly integrated into daily life activities. The design involves an RF-based energy transfer system with optimized rectifiers and antenna arrays, enabling convenient and efficient power delivery for wearable and portable devices.

4. Adhoc Mobile Power Connectivity Using a Wireless Power Transmission Grid

• Authors: P. Gaire, D. Vital, M.R. Khan, C. Chibane, S. Bhardwaj

• Journal: Scientific Reports, Volume 11, Article 17867

• Year: 2021

• Citations: 9

• Paper Summary: This paper explores a novel ad-hoc wireless power transfer (WPT) grid for mobile power connectivity. By implementing beamforming techniques in a patch antenna array, the study enables efficient remote power delivery to mobile devices, enhancing energy accessibility in dynamic environments.

5. Data-Free Solution of Electromagnetic PDEs Using Neural Networks and Extension to Transfer Learning

• Authors: S. Bhardwaj, P. Gaire

• Journal: IEEE Transactions on Antennas and Propagation, Volume 70, Issue 7, Pages 5179-5188

• Year: 2022

• Citations: 6

• Paper Summary: This work investigates the use of neural networks to solve electromagnetic PDEs without training data. The study demonstrates how physics-informed deep learning models can provide accurate solutions for complex wave propagation problems while enabling transfer learning across multiple electromagnetic scenarios.

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