Geometry, Algebra and Physics in Deep Neural Networks
The research group on Geometry, Algebra and Physics in Deep Neural Networks (GAPinDNNs) is based at the Department for Mathematical Sciences at Chalmers University of Technology and the University of Gothenburg. Our vision is to develop a mathematical foundation for deep learning which elevates the field into a theoretically well-grounded science.
News
New Preprint on Steerable Neural ODEs on Homogeneous Spaces
Daniel Persson, together with collaborators Emma Andersdotter and Fredrik Ohlsson at Umeå University, have published a new preprint on Steerable Neural ODEs on Homogeneous Spaces. The paper introduces a novel geometric framework for equivariant neural ODEs on homogeneous spaces, using parallel transport to steer feature vectors transforming under local symmetry groups.
Invited talk at AI4Physics Workshop, Uppsala University
Daniel Persson gave an invited talk on Geometric Deep Learning - From equivariance to weather predictions at the AI4Physics Workshop at Uppsala University. Slides are available here.
New Preprint on The Geometry of Polynomial Group Convolutional Neural Networks
Daniel Persson, together with collaborators Yacoub Hendi and Magdalena Larfors, have published a new preprint on The Geometry of Polynomial Group Convolutional Neural Networks.
Elias in Boston
Our PhD student Elias will spend the spring in Boston on a WASP-funded research visit, working with Maurice Weiler at MIT and Robin Walters at Northeastern University on equivariant neural scaling laws.
Poster session at NeurIPS 2025
Lots of interesting discussion at the poster session here on the first day of NeurIPS 2025 in San Diego! Hampus Linander presented our paper Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks by Longde Huang, Oleksandr Balabanov, Hampus Linander, Mats Granath, Daniel Persson and Jan Gerken.
