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Abstract: Ray tracing is a widely used method to model radio wave propagation in complex environments. However, traditional ray tracing tools are often not differentiable, preventing their use in gradient-based optimization and machine learning pipelines.

Abstract: Earth observation from space is an important scientific and industrial activity that has applications in many sectors. The instruments employed are often large, complex, and expensive. In addition, they generate large amounts of data, which is challenging for storage and transfer purposes.

Résumé: Ces dernières années, les réseaux de neurones profonds sont apparus comme une solution aux problèmes d’imagerie inverse. Ces réseaux sont généralement entraînés à partir de paires d’images : une image dégradée et une image de haute qualité, appelée ‘vérité terrain’.

Abstract: 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.

Abstract: The Grassmannian manifold \(G(k, n)\) serves as a fundamental tool in signal processing, computer vision, and machine learning, where problems often involve classifying, clustering, or comparing subspaces. In this work, we propose a sketching-based approach to approximate Grassmannian kernels using random projections.

Abstract: Differential imaging is a technique to post-process images captured by ground-based telescopes during an observation campaign, in order to make exoplanets in a distant planetary system directly visible and to remove the so-called quasi-static speckles that dramatically affect detection capabilities.

Abstract: With the increasing presence of dynamic scenarios, such as Vehicle-to-Vehicle communications, radio propagation modeling tools must adapt to the rapidly changing nature of the radio channel. Recently, both Differentiable and Dynamic Ray Tracing frameworks have emerged to address these challenges.

Abstract: Representing and processing data in spherical domains presents unique challenges, primarily due to the curvature of the domain, which complicates the application of classical Euclidean techniques. Implicit neural representations (INRs) have emerged as a promising alternative for high-fidelity data representation; however, to effectively handle spherical domains, these methods must be adapted to the inherent geometry of the sphere to maintain both accuracy and stability.

Abstract: Earth observation from space is an important scientific and industrial activity that has applications in many sectors. The instruments employed are often large, complex, and expensive. In addition, they generate large amounts of data, which is challenging for storage and transfer purposes.

Abstract: Radio propagation modeling remains essential for telecommunications research, where Ray Tracing provides a robust tool for simulating electromagnetic interactions in complex environments. In our previous work, we proposed a Machine Learning-assisted Ray Tracing pipeline that uses a generative approach to efficiently sample valid ray paths, significantly reducing computational complexity.