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 …
Interpretable neural networks: principles and applications
In recent years, with the rapid development of deep learning technology, great progress has
been made in computer vision, image recognition, pattern recognition, and speech signal …
been made in computer vision, image recognition, pattern recognition, and speech signal …
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 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 …
Physics-informed neural networks for path loss estimation by solving electromagnetic integral equations
F Jiang, T Li, X Lv, H Rui, D Jin - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Accurately and efficiently modeling wireless channels, especially estimating path loss value,
is crucial to wireless system design and performance optimization. This paper proposes a …
is crucial to wireless system design and performance optimization. This paper proposes a …
Full-range amplitude–phase metacells for sidelobe suppression of metalens antenna using prior-knowledge-guided deep-learning-enabled synthesis
A prior-knowledge-guided deep-learning-enabled (PK-DL) synthesis method is proposed to
design the metacells with the full-range amplitude and phase control for suppressing the …
design the metacells with the full-range amplitude and phase control for suppressing the …
Electromagnetic modeling using an FDTD-equivalent recurrent convolution neural network: Accurate computing on a deep learning framework
In this study, a recurrent convolutional neural network (RCNN) is designed for full-wave
electromagnetic (EM) modeling. This network is equivalent to the finite difference time …
electromagnetic (EM) modeling. This network is equivalent to the finite difference time …
[HTML][HTML] 智能电磁计算的若干进展
刘彻， 杨恺乔， 鲍江涵， 俞文明， 游检卫， 李廉林… - 雷达学报, 2023 - radars.ac.cn
自19 世纪建立麦克斯韦方程以来, 计算电磁学经历了百年的稳定发展, 现已发展出有限差分法,
有限元法, 矩量法等数值算法和高频近似方法, 是现代电子与信息领域的重要基石. 近年来 …
有限元法, 矩量法等数值算法和高频近似方法, 是现代电子与信息领域的重要基石. 近年来 …
An explainable deep learning method for microwave head stroke localization
W Lai, L Guo, K Bialkowski… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
In this article, an explainable deep learning scheme is proposed to tackle microwave
imaging for the task of multiple object localisation. Deep learning has been involved in …
imaging for the task of multiple object localisation. Deep learning has been involved in …
Towards intelligent electromagnetic inverse scattering using deep learning techniques and information metasurfaces
Electromagnetic inverse scattering (EMIS) is uniquely positioned among many inversion
methods because it enables to image the scene in a contactless, quantitative and super …
methods because it enables to image the scene in a contactless, quantitative and super …