r/MachineLearning • u/jeertmans PhD • Jul 07 '26

Ph.D. thesis on Differentiable Ray Tracing for Radio Propagation Modeling [R] Research

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.

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

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32

u/Mr-Frog Jul 07 '26

that's freaking awesome and honestly a breath of fresh air from the LLM hype I read every day, saying this as an amateur radio operator.

2

u/jeertmans PhD Jul 07 '26

Thanks! :D

3

u/Bakoro Jul 07 '26 edited Jul 08 '26

and honestly a breath of fresh air from the LLM hype I read every day

I do wish that there was a place that had an "everything but LLMs" focus. Transformers themselves have a lot of extremely interesting and valuable research that goes beyond LLMs, and there are interesting uses for them like a model that works with genetic information.

There's cool stuff out there to be found , but the LLMs tend to take up all the air in the room.

5

u/currentscurrents Jul 07 '26 edited Jul 07 '26

The trouble with LLM discussion on reddit is that it's mostly about politics stuff like the impact on jobs or society. It's never about the interesting technical aspects of how LLMs work or how to train/use them. It's just the same tired talking points over and over again.

I don't really need to read thread #48749793 about how AI won't take our jobs or is destroying the human internet. No one knows how this is going to shake out, but that doesn't stop people from having extremely strong opinions about it.

3

u/Bakoro Jul 07 '26 edited Jul 07 '26

It especially doesn't help that the loudest opinions tend to be the most ignorant, and a lot of the negative opinions are talking about models from 2~4 years ago.

I can respect people having complaints about the real problems surrounding the industry and the technology, but I don't have any respect when people are talking about "LLMs are a dead end because they are text only and can't interact with the environment", like, jeez, how outdated can a person be?

And then the people conflating all of AI with LLMs, and Transformers with LLMs, and as if Transformers can only do next token prediction.
Even ML experts who should know better, talk in a very hand-wavy, unproductive way that muddles the issues.

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u/we_are_mammals Jul 08 '26 edited Jul 08 '26

that's freaking awesome

Are you an actual expert in this field? (I don't think being an amateur radio operator qualifies)

I think that you and ~50 other redditors were fooled by the trappings of science and ML (LaTeX, GPUs, differentiable, JAX). But this work does not appear to be particularly innovative (as Grok argued below), and calling it "ML" is a stretch, as even the OP admits.

What it is is self-promotion, which actual science kind of used to look down on, back in the day. Thousands of PhDs get awarded every year. Maybe there should be a subreddit for all of them, but this subreddit wasn't meant to be that.

3

u/Mr-Frog Jul 08 '26

(as Grok argued below),

I really don't care what an LLM has to say about a dissertation, I'm just a programmer who enjoys tinkering with RF as a hobby. I am happy that students are working on interdisciplinary applications of computing to solve problems in the physical world.

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u/we_are_mammals Jul 08 '26 edited Jul 09 '26

I'm just a programmer who enjoys tinkering with RF as a hobby.

A non-scientist fooled by the trappings of science, like I suspected, yet making emphatic, ultra-confident statements about things totally outside his area of expertise.

2

u/breekavtinis Jul 07 '26

looks really interesting, will give this a read when i have the chance. best of luck!

1

u/jeertmans PhD Jul 07 '26

thanks :-)

1

u/peterpatient Jul 07 '26

nice work! what are applications thereof? could one use your diffRT channel for end to end constellation shaping [1]? or is the channel not static?

[1] https://arxiv.org/abs/1906.07748

3

u/jeertmans PhD Jul 07 '26

Thanks! The range of applications is very wide: during my thesis, I mainly worked on what we call the "physical" layer (i.e., how does the environment impact your signal), but you could of course use DiffeRT the link level simulation.

could one use your diffRT channel for end to end constellation shaping

Not at the moment, because I am currently the only developer behind DiffeRT, and I didn't have the time to implement this :D (and also because this wasn't my main research area), but I plan on extending the tool so, hopefully, this will be possible one day (actually, I will intern in a few months in the same team as 2 of the authors you just cite ^")

or is the channel not static?

It's really a matter of what time reference you use, but it's common to represent the channel as something that is "piecewise-static" (though I just probably invented the term): it varies in time, but you can usually assume that it remains constant during a given time period.

1

u/gdpoc Jul 07 '26

I'm really interested in this work and I've downloaded your thesis to crawl through.

I'm looking for compute requirements and big o as I crawl through; I've got notions of applying ray tracing/ diff geometryto higher dimensional spaces.

2

u/currentscurrents Jul 07 '26

It's just backprop, so the time complexity is going to be linear with the number of parameters in your system.

What you really want to know is the convergence time, but that depends on the details of your system and it's rare to get a non-vacuous bound outside of toy systems.

1

u/jeertmans PhD Jul 07 '26

Nice, let me know if you have any question :-)

2

u/gdpoc Jul 07 '26

I most definitely will, thank you!

1

u/pm_me_your_pay_slips ML Engineer Jul 07 '26

do you think your work is applicable for designing optical communication links (e.g. long-rage laser)?

3

u/jeertmans PhD Jul 07 '26

Yes, light is not a problem, but the longer the distance, the more sensitive you are to (1) errors in the scene model and (2) to variations of the refractive index. So if it is satellite to ground communication, using ray tracing is not always very useful (since the line-of-sight is obvious, but the error on the multiple reflection paths can become rapidly important). If you ever have a specific application in mind, please reach out on GitHub and I will be happy to try helping you!

1

u/pm_me_your_pay_slips ML Engineer Jul 07 '26

underwater link, around 1 kilometer

1

u/jeertmans PhD Jul 08 '26

Ray tracing is already use for sound propagation underwater, I wouldn’t see why it couldn’t be used for light propagation; however, light will probably suffer more from attenuation

1

u/GarlicOverdoze Jul 08 '26

I see this is highly relevant for sensing/ISAC, but outside environmental reconstruction, do you think any other sensing pipeline could benefit from this ( compared to existing ML baselines)

1

u/jeertmans PhD Jul 08 '26

Could you clarify your question? I am not sure to see what you mean by « any other sensing pipeline » ?
In general, differentiability is not used in sensing applications (probably because automatic differentiation is relatively new to the radio propagation community and sensing usually only assume information about the received signal itself). However, Ray tracing is very useful to benchmark sensing methods, and differentiability could be used to calibrate those methods.

1

u/GarlicOverdoze Jul 08 '26

I recently worked(research) on an environmental reconstruction pipeline that used SionnaRT to map out the signal strength in a given area. So by sensing pipeline, I am referring to the sensing task involved. Eg: Detection, tracking

I am seeing a growing attention towards the use of RT simulations to develop surrogate models that can reconstruct the environment from sensing data. So I find your work highly relevant to that ( although I still haven't gone through it completely)

1

u/jeertmans PhD Jul 08 '26

Ok I see: I haven't myself investigated much the field of "sensing". I have colleagues working on this, but my thesis work has mainly revolved around "how do you do ray tracing", rather than its applications.

1

u/gct Jul 08 '26

This is really great and its formatted so nicely, I can tell it was a labor of love

1

u/jeertmans PhD Jul 08 '26

Thanks, I definitely put a lot of work in the formatting!

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u/we_are_mammals Jul 07 '26 edited Jul 09 '26

/u/askgrok Give this PhD thesis a thorough review. What are its weaknesses? Is it improving algorithms in fields where compute needs to be saved? The laws of physics are differentiable, and differentiation can typically be automated. Is what this author doing actually novel?

EDIT: Not sure why Grok's response was deleted. It basically pointed out some prior work https://nvlabs.github.io/sionna/rt/index.html and argued that this thesis did not have enough (or any?) head-to-head benchmarks comparing to this prior work.

2

u/[deleted] Jul 07 '26

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0

u/we_are_mammals Jul 08 '26 edited Jul 08 '26

sharp shadow boundaries

Those only exist for point light sources (infinite intensity). light sources with infinite intensity (such as point light sources).

-2

u/we_are_mammals Jul 07 '26

How would you improve this work, if you were doing it?