تقرير
Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases
| العنوان: | Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases |
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| المؤلفون: | Shan, Tao, Lin, Zhichao, Song, Xiaoqian, Li, Maokun, Yang, Fan, Xu, Zhensheng |
| المصدر: | IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 818-829, Jan. 2023 |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Computational Physics, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing, 35Q61, I.2.6, G.1.8, G.1.3 |
| الوصف: | 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). NeuralBIM employs independent convolutional neural networks (CNNs) to learn the alternate update rules of two different candidate solutions regarding the residuals. Two different schemes are presented in this paper, including the supervised and unsupervised learning schemes. With the data set generated by the method of moments (MoM), supervised NeuralBIM are trained with the knowledge of total fields and contrasts. Unsupervised NeuralBIM is guided by the physics-embedded objective function founding on the governing equations of ISPs, which results in no requirement of total fields and contrasts for training. Numerical and experimental results further validate the efficacy of NeuralBIM. Comment: This preprint has been published in IEEE Transactions on Antennas and Propagation on 01 November 2022. Please cite the final published version as [T. Shan et al., "Neural Born Iterative Method for Solving Inverse Scattering Problems: 2D Cases," in IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 818-829, Jan. 2023, doi: 10.1109/TAP.2022.3217333.] |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/TAP.2022.3217333 |
| الوصول الحر: | http://arxiv.org/abs/2112.09831Test |
| رقم الانضمام: | edsarx.2112.09831 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/TAP.2022.3217333 |
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