A Physics-Based Deep Learning to Extend Born Approximation Validity to Strong Scatterers

IF 4.6 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Antennas and Propagation Pub Date : 2024-10-01 DOI:10.1109/TAP.2024.3467700
Leila Ahmadi;Amir Ahmad Shishegar
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Abstract

In this study, we present a novel approach to address nonweak scattering problems by integrating deep learning (DL) into the Born series. Typically, the first-order Born approximation (BA) is limited to cases where the contrast between the scatterer and the background medium is exceptionally low. While higher-order terms in the Born series can be used for higher contrasts, convergence issues may arise due to highly oscillatory factors in Green’s function. To overcome this limitation, we introduce a physics-based DL method inspired by the Born series, which effectively predicts the distribution of the complex electromagnetic field. The proposed series assures convergence when scattering occurs from high-contrast objects, due to the use of a learning-based forward operator. Exploiting the physics-based nature of our model, we adopt a simple convolutional neural network (CNN) architecture, requiring significantly fewer training data. Our results demonstrate very good generalization capabilities of the proposed approach, showcasing its ability to handle unseen background fields and profiles. We deem this innovative series as an extension of the Born series that can be effectively employed in highly nonlinear problems.
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在本研究中,我们提出了一种新方法,通过将深度学习(DL)集成到 Born 序列中来解决非弱散射问题。通常情况下,一阶 Born 近似(BA)仅限于散射体与背景介质对比度极低的情况。虽然 Born 序列中的高阶项可以用于更高的对比度,但由于格林函数中的高度振荡因素,可能会出现收敛问题。为了克服这一限制,我们引入了一种基于物理学的 DL 方法,其灵感来自于 Born 系列,能有效预测复电磁场的分布。由于使用了基于学习的前向算子,当高对比度物体发生散射时,所提出的数列可确保收敛性。利用模型基于物理的特性,我们采用了简单的卷积神经网络(CNN)架构,大大减少了所需的训练数据。我们的结果表明,所提出的方法具有很好的泛化能力,能够处理未见的背景场和轮廓。我们将这种创新的序列视为博恩序列的扩展,可以有效地用于高度非线性问题。
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来源期刊
CiteScore
10.40
自引率
28.10%
发文量
968
审稿时长
4.7 months
期刊介绍: IEEE Transactions on Antennas and Propagation includes theoretical and experimental advances in antennas, including design and development, and in the propagation of electromagnetic waves, including scattering, diffraction, and interaction with continuous media; and applications pertaining to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques
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Table of Contents 2024 Industrial Innovation Award 2024 Distinguished Industry Leader Award 2024 IEEE AP-S Piergiorgio L.E. Uslenghi Prize Paper Award 2024 IEEE AP-S Harold A. Wheeler Application Prize Paper Award
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