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 …

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 …

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 …

Limited throughput is a key challenge in in vivo deep tissue imaging using nonlinear optical
microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow …

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 …

This article proposes a learning-based noniterative method to solve electromagnetic (EM)
scattering problems utilizing pix2pix, a popular generative adversarial network (GAN) …

Biomedical imaging is a relevant noninvasive technique aimed at generating an image of
the biological structure under analysis. The arising visual representation of the …

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 …

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 …

Abstract Machine learning has been demonstrated to be extremely promising in solving
inverse problems, but deep learning algorithms require enormous training samples to obtain …