HackerPapers

arXiv:1908.10507physics.optics

Forward-Mode Differentiation of Maxwell's Equations

Tyler W Hughes, Ian A D Williamson, Momchil Minkov, Shanhui Fan

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

We present a previously unexplored forward-mode differentiation method for Maxwell's equations, with applications in the field of sensitivity analysis. This approach yields exact gradients and is similar to the popular adjoint variable method, but provides a significant improvement in both memory and speed scaling for problems involving several output parameters, as we analyze in the context of finite-difference time-domain (FDTD) simulations. Furthermore, it provides an exact alternative to numerical derivative methods, based on finite-difference approximations. To demonstrate the usefulness of the method, we perform sensitivity analysis of two problems. First we compute how the spatial near-field intensity distribution of a scatterer changes with respect to its dielectric constant. Then, we compute how the spectral power and coupling efficiency of a surface grating coupler changes with respect to its fill factor.

arXiv comments
13 pages, 4 figures
Journal reference
ACS Photonics, vol. 6, no. 11, pp. 3010-3016, Oct. 2019
DOI
doi:10.1021/acsphotonics.9b01238
Subjects
physics.opticsOptics math.NANumerical Analysis physics.comp-phComputational Physics

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Sep 2019 to Sep 2019. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

  • Simulating Maxwell's Equations with Automatic Differentiation

    We wrote core Maxwell's Equations solvers using the HIPS autograd package, and defined custom primitives for performing backpropagation through electromagnetic simulations. This allows one to effortlessly perform gradient-based optimization of optical devices, as well as plug electromagnetic simulations into a machine learning model. For more details, our github repo: https://github.com/twhughes/ceviche and arxiv preprint: https://arxiv.org/abs/1908.10507

Connected through shared HN threads, not inferred similarity or a recommendation.