In the recent years, a dramatic boost of the research is observed at the junction of photonics,
machine learning and artificial intelligence. A new methodology can be applied to the …

Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus
users on defining complex, high-level optical functionalities while leveraging machines to …

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

In this article, an unrolling algorithm of the iterative subspace-based optimization method
(SOM) is proposed for solving full-wave inverse scattering problems (ISPs). The unrolling …

Based on the computational framework of physics-informed neural networks (PINNs), an
unsupervised deep learning method is developed for inverse problems, which features good …

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 …

Microwave imaging is emerging as an alternative modality to conventional medical
diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced …

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 …

Recently, deep-learning (DL) approaches have shown their advantages in solving scientific
problems including full-wave nonlinear inverse problems. However, these data-driven …

Recently, deep learning methods have made significant success in inverse scattering
problems (ISPs). However, learning approaches that work in different spaces, such as …