Physics-embedded machine learning for electromagnetic data imaging: Examining three types of data-driven imaging methods
Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine,
geophysics, and various industries. It is an ill-posed inverse problem whose solution is …
geophysics, and various industries. It is an ill-posed inverse problem whose solution is …
Physics-informed supervised residual learning for electromagnetic modeling
In this study, physics-informed supervised residual learning (PhiSRL) is proposed to enable
an effective, robust, and general deep learning framework for 2-D electromagnetic (EM) …
an effective, robust, and general deep learning framework for 2-D electromagnetic (EM) …
New and Emerging Directions in the Fields of Antennas and Propagation
In this invited paper, the New Technology Directions Committee (NTDC) of the IEEE
Antennas and Propagation Society (AP-S) presents new and emerging research directions …
Antennas and Propagation Society (AP-S) presents new and emerging research directions …
Classification with electromagnetic waves
E Simsek, HR Manyam - IET Microwaves, Antennas & …, 2024 - Wiley Online Library
The integration of neural networks and machine learning techniques has ushered in a
revolution in various fields, including electromagnetic inversion, geophysical exploration …
revolution in various fields, including electromagnetic inversion, geophysical exploration …
Physics-informed supervised residual learning for 2-D inverse scattering problems
In this communication, we propose a new physics-constrained approach to solve 2-D
inverse scattering problems (ISPs) by extending physics-informed supervised residual …
inverse scattering problems (ISPs) by extending physics-informed supervised residual …
Physics embedded machine learning for electromagnetic data imaging
Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine,
geophysics, and various industries. It is an ill-posed inverse problem whose solution is …
geophysics, and various industries. It is an ill-posed inverse problem whose solution is …
Solving Combined Field Integral Equations with Physics-informed Graph Residual Learning for EM Scattering of 3D PEC Targets
In this study, physics-informed graph residual learning (PhiGRL) is proposed as an effective
and robust deep learning (DL)-based approach for 3-D electromagnetic (EM) modeling …
and robust deep learning (DL)-based approach for 3-D electromagnetic (EM) modeling …
A Physics-Based Deep Learning to Extend Born Approximation Validity to Strong Scatterers
L Ahmadi, AA Shishegar - IEEE Transactions on Antennas and …, 2024 - ieeexplore.ieee.org
In this study, we present a novel approach to address non-weak scattering problems by
integrating deep learning into the Born series. Typically, the first order Born approximation is …
integrating deep learning into the Born series. Typically, the first order Born approximation is …
GPR Mapping of Cavities in Complex Scenarios with a Combined Time–Depth Conversion
R Persico, I Catapano, G Esposito, G Morelli… - Sensors, 2024 - mdpi.com
The paper deals with a combined time–depth conversion strategy able to improve the
reconstruction of voids embedded in an opaque medium, such as cavities, caves, empty …
reconstruction of voids embedded in an opaque medium, such as cavities, caves, empty …
Contrast source inversion of sparse targets through multi-resolution Bayesian compressive sensing
The retrieval of non-Born scatterers is addressed within the contrast source inversion (CSI)
framework by means of a novel multi-step inverse scattering method that jointly exploits prior …
framework by means of a novel multi-step inverse scattering method that jointly exploits prior …