  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.
