论文标题
通过深度微分森林学习FDTD方法的基于非平地的PML
Learning Unsplit-field-based PML for the FDTD Method by Deep Differentiable Forest
论文作者
论文摘要
基于有限差异时间域（FDTD）的替代基于非平面的吸收边界条件（ABC）计算方法是基于深层区分的森林有效提出的。引入了深度微分森林（DDF）模型，以替换FDTD计算过程中常规匹配的层（PML）ABC。采用传统PML接口上的现场组件数据来训练基于DDF的PML模型。 DDF具有树木和神经网络的优势。它的树结构易于使用，并为数值PML数据解释。它具有完全不同的神经网络。 DDF可以通过深度学习的强大技术培训。因此，与传统的PML实施相比，提出的方法可以大大降低FDTD物理域的大小以及由于新型模型而引起的FDTD的计算复杂性，该模型仅涉及边界层的单细胞厚度。已经进行了数值模拟，以基准提出的方法的性能。数值结果表明，所提出的方法不仅可以轻松替换传统的PML，而且还可以以令人满意的数值准确性和与FDTD的兼容性集成到FDTD计算过程中。
Alternative unsplit-filed-based absorbing boundary condition (ABC) computation approach for the finite-difference time-domain (FDTD) is efficiently proposed based on the deep differentiable forest. The deep differentiable forest (DDF) model is introduced to replace the conventional perfectly matched layer (PML) ABC during the computation process of FDTD. The field component data on the interface of traditional PML are adopted to train the DDF-based PML model. DDF has the advantages of both trees and neural networks. Its tree structure is easy to use and explain for the numerical PML data. It has full differentiability like neural networks. DDF could be trained by powerful techniques from deep learning. So compared to the traditional PML implementation, the proposed method can greatly reduce the size of FDTD physical domain and the calculation complexity of FDTD due to the novel model which only involves the one-cell thickness of boundary layer. Numerical simulations have been carried out to benchmark the performance of the proposed approach. Numerical results illustrate that the proposed method can not only easily replace the traditional PML, but also be integrated into the FDTD computation process with satisfactory numerical accuracy and compatibility to the FDTD.
