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Tensor-Train FDTD: Implementation Aspects and Performance Analysis
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DOI：10.1109/TAP.2025.3551598.png)
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Abstract
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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.
Keywords:
Finite-difference time-domain (FDTD)
tensor decomposition
tensor train (TT)
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