no code implementations • 2 Sep 2024 • Daoqi Liu, Tao Shan, Maokun Li, Fan Yang, Shenheng Xu
The multi-frequency NeuralBIM method explores a novel inversion method for multi-frequency EM data and provides an effective solution for the electromagnetic ISP of multi-frequency data.
no code implementations • 1 Jul 2023 • Shuzhe Chen, Li Li, Zhichao Lin, Ke Zhang, Ying Gong, Lu Wang, Xu Wu, Maokun Li, Yuanlin Song, Fan Yang, Shenheng Xu
A simple convolutional neural network is used for classification.
no code implementations • 11 Nov 2022 • Changhao Liu, Fan Yang, Maokun Li, Shenheng Xu
Recently, artificial neural network empowered inverse design for metasurfaces has been developed that can design on-demand meta-atoms with diverse shapes and high performance, where the design process based on artificial intelligence is fast and automatic.
no code implementations • 26 Jul 2022 • Rui Guo, Tianyao Huang, Maokun Li, Haiyang Zhang, Yonina C. Eldar
To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work.
no code implementations • 18 Dec 2021 • Tao Shan, Zhichao Lin, Xiaoqian Song, Maokun Li, Fan Yang, Zhensheng Xu
In this paper, we propose the neural Born iterative method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iterative method (TBIM).
no code implementations • IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 20, NO. 4, APRIL 2021 • Rui Guo, Zhichao Lin, Tao Shan, Maokun Li, Fan Yang, Shenheng Xu, Aria Abubakar
Abstract—Solving the combined field integral equation (CFIE) for the large-scale scattering problem is computationally expensive.