Demonstrating DiffeRT: An Open-Source Library for Optimizing Radio Networks with Differentiable Ray Tracing

This proposal presents a demonstration of DiffeRT, an open-source Differentiable Ray Tracing toolbox designed for Machine Learning and optimization applications in the context of radio propagation. Built on the JAX framework for differentiable programming and the Equinox library as a Machine Learning framework, DiffeRT extends its predecessor, DiffeRT2d, to support 3D scenes with enhanced performance and higher- level electromagnetic field computation features. The toolbox is fully scalable to GPUs and TPUs, enabling fast and efficient ray tracing for complex scenarios. Unlike existing alternatives like Sionna, DiffeRT focuses exclusively on ray-tracing functionalities, offering both low-level and high-level APIs for tailored solutions. This demonstration will showcase DiffeRT's unique capabilities, highlighting its suitability for a range of Machine Learning and optimization workflows. The tool is accessible via the Python Package Index (PyPI), and its source code can be obtained from its public repository: https://github.com/jeertmans/DiffeRT.