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Artificial neural networks for microwave computer-aided design: The state of the art
This article presents an overview of artificial neural network (ANN) techniques for a
microwave computer-aided design (CAD). ANN-based techniques are becoming useful for …
microwave computer-aided design (CAD). ANN-based techniques are becoming useful for …
A review of deep learning approaches for inverse scattering problems (invited review)
In recent years, deep learning (DL) is becoming an increasingly important tool for solving
inverse scattering problems (ISPs). This paper reviews methods, promises, and pitfalls of …
inverse scattering problems (ISPs). This paper reviews methods, promises, and pitfalls of …
Artificial intelligence: New frontiers in real-time inverse scattering and electromagnetic imaging
In recent years, artificial intelligence (AI) techniques have been developed rapidly. With the
help of big data, massive parallel computing, and optimization algorithms, machine learning …
help of big data, massive parallel computing, and optimization algorithms, machine learning …
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 …
Deep learning-based inversion methods for solving inverse scattering problems with phaseless data
Without phase information of the measured field data, the phaseless data inverse scattering
problems (PD-ISPs) counter more serious nonlinearity and ill-posedness compared with full …
problems (PD-ISPs) counter more serious nonlinearity and ill-posedness compared with full …
Physics embedded deep neural network for solving full-wave inverse scattering problems
In this work, we design an iterative deep neural network to solve full-wave inverse scattering
problems (ISPs) in the 2-D case. Forward modeling neural networks that predict the …
problems (ISPs) in the 2-D case. Forward modeling neural networks that predict the …
Learning-based fast electromagnetic scattering solver through generative adversarial network
This article proposes a learning-based noniterative method to solve electromagnetic (EM)
scattering problems utilizing pix2pix, a popular generative adversarial network (GAN) …
scattering problems utilizing pix2pix, a popular generative adversarial network (GAN) …
Machine learning in electromagnetics with applications to biomedical imaging: A review
Biomedical imaging is a relevant noninvasive technique aimed at generating an image of
the biological structure under analysis. The arising visual representation of the …
the biological structure under analysis. The arising visual representation of the …
Nonlinear S-parameters inversion for stroke imaging
Stroke identification by means of microwave tomography requires a very accurate
reconstruction of the dielectric properties inside patient's head. This is possible when a …
reconstruction of the dielectric properties inside patient's head. This is possible when a …
ANNs for fast parameterized EM modeling: The state of the art in machine learning for design automation of passive microwave structures
Artificial neural networks (ANNs) are information processing systems, with their design
inspired by studies of the ability of the human brain to learn from observations and …
inspired by studies of the ability of the human brain to learn from observations and …