English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Alternative unsplit-filed-based absorbing boundary condition (ABC)computation approach for the finite-difference time-domain (FDTD) isefficiently proposed based on the deep differentiable forest. The deepdifferentiable forest (DDF) model is introduced to replace the conventionalperfectly matched layer (PML) ABC during the computation process of FDTD. Thefield component data on the interface of traditional PML are adopted to trainthe DDF-based PML model. DDF has the advantages of both trees and neuralnetworks. Its tree structure is easy to use and explain for the numerical PMLdata. It has full differentiability like neural networks. DDF could be trainedby powerful techniques from deep learning. So compared to the traditional PMLimplementation, the proposed method can greatly reduce the size of FDTDphysical domain and the calculation complexity of FDTD due to the novel modelwhich only involves the one-cell thickness of boundary layer. Numericalsimulations have been carried out to benchmark the performance of the proposedapproach. Numerical results illustrate that the proposed method can not onlyeasily replace the traditional PML, but also be integrated into the FDTDcomputation process with satisfactory numerical accuracy and compatibility tothe FDTD.

Learning Unsplit-field-based PML for the FDTD Method by Deep Differentiable Forest