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COST INTERACT - DiffeRT2d: A Differentiable Ray Tracing Python Framework for Radio Propagation

Presentation of the DiffeRT2d toolbox to the COST INTERACT community.

In the preparation of our submission to the Journal of Open Source Software, I presented the DiffeRT2d Python library at the COST INTERACT meeting, held in Helsinki. This toolbox was used to define and train a Machine Learning model to help tracing paths faster, that we documented in other document presented during this meeting.

Moreover, this toolbox implements both our Min-Path-Tracing method (Eertmans et al., 2023) and our smoothing technique (Eertmans et al., 2024).

Slides

The following slides are made with RevealJS and are interactive!

Use basic keys like LEFT and RIGHT to navigate through slides, or F to go full screen.

The slides were generated using the Manim (The Manim Community Developers, 2022) animation engine and Manim Slides (Eertmans, 2023), one of my open source projects, to combine the animations into slides and later convert them in a RevealJS .html file.

References

  1. Eertmans, J., Oestges, C., & Jacques, L. (2023). Min-Path-Tracing: A Diffraction Aware Alternative to Image Method in Ray Tracing. 2023 17th European Conference on Antennas and Propagation (EuCAP), 1–5. https://doi.org/10.23919/EuCAP57121.2023.10132934
  2. Eertmans, J., Oestges, C., & Jacques, L. (2024). Fully Differentiable Ray Tracing via Discontinuity Smoothing for Radio Network Optimization. 2024 18th European Conference on Antennas and Propagation (EuCAP), 1–5.
  3. The Manim Community Developers. (2022). Manim – Mathematical Animation Framework (Version v0.17.2). https://www.manim.community/
  4. Eertmans, J. (2023). Manim Slides: A Python package for presenting Manim content anywhere. Journal of Open Source Education, 6(66), 206. https://doi.org/10.21105/jose.00206

Source code

Available on GitHub: jeertmans/DiffeRT2d@papers/joss/slides.md.

This post is licensed under CC BY 4.0 by the author.