Ionosphere geodesic FDTD

ionosphere-fdtd is a NumPy and PyTorch implementation of a three-dimensional geodesic finite-difference time-domain model for the Earth–ionosphere waveguide. It combines an icosahedral primal/dual surface mesh with staggered radial Yee planes, conductive materials, localized current sources, receiver sampling, and two- and three-dimensional visualization.

FDTD pulse propagation from Gwangju on the geodesic Earth-ionosphere grid

Highlights

  • NumPy CPU and PyTorch CPU, CUDA, and Apple Metal/MPS backends
  • Full-spherical radial curls and conservative CFL selection
  • Exponential conductive integration with a legacy trapezoidal option
  • Configurable geodesic and radial grids, materials, sources, and anomalies
  • Surface maps, radial sections, receiver traces, interactive 3-D views, and animations
  • Analytic Maxwell verification and Simpson–Taflove reproduction workflows

Quick start

Python 3.11 or newer is required.

uv sync --extra test --extra visualization --extra pytorch
uv run ionosphere --steps 200

Minimal Python usage:

from ionosphere_fdtd import GeodesicFDTD, GaussianCurrent, SimulationConfig

simulation = GeodesicFDTD(
    SimulationConfig(subdivision=2, radial_cells=24),
    source=GaussianCurrent(carrier_frequency_hz=20.0),
)
simulation.step(1000)
print(simulation.diagnostics())

Documentation

Development

uv run --extra test --extra visualization --extra pytorch pytest -q

Verification workflows are kept outside the distributed runtime package and run from a source checkout. The linked verification reports contain their commands, acceptance criteria, and current results.

References

  1. D. A. Randall et al., “Climate Modeling with Spherical Geodesic Grids,” Computing in Science & Engineering, 4(5), 32–41, 2002.
  2. J. J. Simpson and A. Taflove, “Three-dimensional FDTD modeling of impulsive ELF propagation about the entire Earth-sphere,” IEEE TAP, 52(2), 443–451, 2004.
  3. J. J. Simpson, R. P. Heikes, and A. Taflove, “FDTD modeling of a novel ELF radar for major oil deposits using a three-dimensional geodesic grid of the Earth-ionosphere waveguide,” IEEE TAP, 54(6), 1734–1741, 2006.
  4. A. Taflove and S. C. Hagness, Computational Electrodynamics: The Finite-Difference Time-Domain Method, 3rd ed., Chapter 3, 2005.

License

Copyright 2026 Kyungwon Chun.

Licensed under the Apache License 2.0. See NOTICE for the project copyright notice.

Project links

Data verified by PyPI on Aug 14, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ruddyscent from gravatar.com ruddyscent

Credits

Author: Kyungwon Chun

GitHub Statistics

Data verified by PyPI on Aug 14, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.11

Provides Extra

test pytorch tensorboard visualization verification

Tags

electromagnetics fdtd geodesic-grid ionosphere maxwell-equations

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ionosphere_fdtd-0.1.0.tar.gz (65.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ionosphere_fdtd-0.1.0-py3-none-any.whl (53.6 kB view details)

Uploaded Python 3

File details

Details for the file ionosphere_fdtd-0.1.0.tar.gz.

File metadata

  • Download URL: ionosphere_fdtd-0.1.0.tar.gz
  • Upload date:
  • Size: 65.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for ionosphere_fdtd-0.1.0.tar.gz
Algorithm Hash digest
SHA256 804d118695654c80fa09a12926bdc7be5674d6d2585e90a371df49430a4b329b
MD5 011b305e446ca8a49bb0353bd69a341e
BLAKE2b-256 b350f2c6ba1ba39e141757b97145122c49793f897e90b899f8ae7e5ca0e2f9df

See more details on using hashes here.

Provenance

The following attestation bundles were made for ionosphere_fdtd-0.1.0.tar.gz:

Publisher: release.yml on ruddyscent/ionosphere-fdtd

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file ionosphere_fdtd-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: ionosphere_fdtd-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 53.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for ionosphere_fdtd-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 a5325eaa0c68bcf2581997bdc99458977ee0d569dfb6efbf2496ba7fda925d2c
MD5 54dbeb32a9b8c4010f281d3df2616f55
BLAKE2b-256 a16516b75e2b375f4e3bc3b3a276a984f9eb98a590bc2016bbaa1e439d673ec1

See more details on using hashes here.

Provenance

The following attestation bundles were made for ionosphere_fdtd-0.1.0-py3-none-any.whl:

Publisher: release.yml on ruddyscent/ionosphere-fdtd

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.2.0

2 files

0.1.1

2 files

This release

0.1.0 This release

2 files

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page