jan/fdfd_tools
2
2
Fork 1
Code Issues Pull Requests Releases Wiki Activity
*DEPRECATED* Tools for optical simulations
139 Commits 8 Branches 0 Tags 1.8 MiB
Python 100%
b5ad284966
Go to file
jan b5ad284966 dx_prop -> prop_phase 
propagation direction wavenumber might be different from
operator-derived (2D) wavenumber due to numerical dispersion, so lump it
in with dx_prop
2019-09-05 22:35:23 +02:00
examples in progress 2019-08-30 22:03:54 -07:00
meanas dx_prop -> prop_phase 2019-09-05 22:35:23 +02:00
.gitignore initial development 2016-05-30 22:30:45 -07:00
LICENSE.md add license 2016-04-13 04:06:15 -07:00
README.md rename to meanas and split fdtd/fdfd 2019-08-04 13:48:41 -07:00
setup.py rename to meanas and split fdtd/fdfd 2019-08-04 13:48:41 -07:00

README.md

meanas

meanas is a python package for electromagnetic simulations

This package is intended for building simulation inputs, analyzing simulation outputs, and running short simulations on unspecialized hardware. It is designed to provide tooling and a baseline for other, high-performance purpose- and hardware-specific solvers.

Contents

  • Finite difference frequency domain (FDFD)
    • Library of sparse matrices for representing the electromagnetic wave equation in 3D, as well as auxiliary matrices for conversion between fields
    • Waveguide mode operators
    • Waveguide mode eigensolver
    • Stretched-coordinate PML boundaries (SCPML)
    • Functional versions of most operators
    • Anisotropic media (limited to diagonal elements eps_xx, eps_yy, eps_zz, mu_xx, ...)
    • Arbitrary distributions of perfect electric and magnetic conductors (PEC / PMC)
  • Finite difference time domain (FDTD)
    • Basic Maxwell time-steps
    • Poynting vector and energy calculation
    • Convolutional PMLs

This package does not provide a fast matrix solver, though by default meanas.fdfd.solvers.generic(...) will call scipy.sparse.linalg.qmr(...) to perform a solve. For 2D FDFD problems this should be fine; likewise, the waveguide mode solver uses scipy's eigenvalue solver, with reasonable results.

For solving large (or 3D) FDFD problems, I recommend a GPU-based iterative solver, such as opencl_fdfd or those included in MAGMA). Your solver will need the ability to solve complex symmetric (non-Hermitian) linear systems, ideally with double precision.

Installation

Requirements:

  • python 3 (tests require 3.7)
  • numpy
  • scipy

Install with pip, via git:

pip install git+https://mpxd.net/code/jan/meanas.git@release

Use

See examples/ for some simple examples; you may need additional packages such as gridlock to run the examples.