u/jeertmans

Hi everyone, I recently finished my Ph.D. thesis on Differentiable Ray Tracing for Radio Propagation Modeling. Instead of just compiling my published papers, I tried to write it as an accessible, self-contained textbook for anyone interested in the intersection of radio propagation simulation, autodiff, and ML.

• Permanent handle: https://hdl.handle.net/2078.5/278727
• Repo with TeX source files

While my research focuses on wireless communications rather than pure ML, I think it fits right in here. A major part of the project revolves around automatic differentiation. By taking frameworks like JAX out of their traditional ML context and integrating differentiability into a ray tracing pipeline, we can compute exact gradients through complex physical environments. This allows us to solve inverse problems and directly train machine learning models, which is currently a hot topic in next-gen wireless design.

To make the physics and the math easy to digest, the manuscript is split into three parts:

• Understanding: The physics fundamentals (electromagnetic theory, geometrical optics, and diffraction).
• Building: The algorithmic core, including GPU-accelerated path tracing and the discontinuity smoothing techniques you need to actually make differentiable simulations stable.
• Using: Practical applications like channel modeling, localization, material calibration, and ML-assisted generative path sampling.

A major focus of my thesis is the link between scientific research and reproducible open-source software. On that note, I want to give a massive shoutout to Patrick Kidger (u/patrickkidger). His own thesis inspired me to go the "textbook way" for my manuscript, and I heavily relied on his fantastic JAX packages (jaxtyping, equinox, and optimistix) when developing my open-source libraries, such as DiffeRT.

I hope you find it an interesting read! I'd be happy to answer any questions in the comments about differentiable simulation, ray tracing, or building ray tracing engines in JAX :-)

If you are curious, you can watch the presentation slides and video teaser here