张量训练FDTD：实现方面和性能分析

IF 4.6 1区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Antennas and Propagation Pub Date : 2025-03-27 DOI:10.1109/TAP.2025.3551598

Qiping Zhou;Fernando L. Teixeira

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引用次数: 0

摘要

张量训练（TT）分解在CPU时间和内存存储方面具有显著提高有限差分时域（FDTD）算法性能的潜力。为此，我们将tt格式的FDTD实现扩展到包含完美匹配层（PML）边界的情况。我们评估了tt格式FDTD实现在不同容错级别下的性能。特别地，分析了精度和效率之间的权衡。此外，提出了一种正则化方法来控制tt格式FDTD仿真中由于PML吸收和不同源激励引起的跨域高度不同的场振幅水平的秩增长。

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Tensor-Train FDTD: Implementation Aspects and Performance Analysis

Tensor-train (TT) decompositions have the potential to significantly improve the performance of finite-difference time-domain (FDTD) algorithms in terms of CPU time and memory storage. To this end, we extend TT-format FDTD implementations to cases incorporating perfectly matched layer (PML) boundaries. We assess the performance of TT-format FDTD implementations for different error tolerance levels. In particular, the tradeoff between accuracy and efficiency is analyzed. Additionally, a regularization approach is proposed to control rank growth in TT-format FDTD simulations with highly disparate field amplitude levels across the domain brought forth by PML absorption and diverse source excitations.

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来源期刊

IEEE Transactions on Antennas and Propagation 工程技术-电信学

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