Geometric Deep Learning

We generalise steerable E(3) equivariant graph neural networks such that node and edge updates are able to leverage covariant information. Published at ICLR 2022 (Spotlight).

We generalize graph neural network based simulations of Lagrangian dynamics to complex boundaries as encountered in daily life engineering setups. Published at AAAI 2023.

My passion for Geometric Deep Learning can be unmistakenly traced back to my physics background. I have contributed to the fields of graph neural networks, equivariant architectures, and neural PDE solvers. Furthermore, I have lead efforts to introduce Lie Point Symmetries, and, most recently, Clifford (Geometric) Algebras into the Deep Learning community.