Geometry, Algebra and Physics in Deep Neural Networks

Our work on Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks is now on the arXiv! In this paper, we combine lattice gauge equivariant networks with a novel training mechanism to learn topological invariants (Chern numbers) of topological insulators. This paper combines several beautiful topics in machine learning, physics and mathematics.

First author is our master’s student Longde Huang. Congratualtions to his first publication! From our group, Hampus Linander, Daniel Persson and Jan Gerken were also involved. Thanks to our phyiscs-collaborators Oleksandr Balabanov (then at Stockholm University) and Mats Granath (University of Gothenburg) for their expertise and a fun collaboration!

Elias Nyholm is one of the organisers of the LoG Meetup Sweden in Uppsala this year. This two-day workshop is the official local meetup of the online LoG Conference and will consist of keynote talks, contributed talks and a poster session.

The group had a strong presence at the WASP Winter Conference in Norrköping this year, including poster presentations by Elias, Philipp and Oscar.

Inauguration lecture for Daniel Persson’s promotion to full professor of mathematics.

Interview with Daniel Persson on the occasion of his upcoming promotion to full professor of mathematics.

Public lecture by Daniel Persson for high school students at Hulebäcksgymnasiet.

Paper by Daniel Persson together with Emma Andersdotter and Fredrik Ohlsson at Umeå University.