Equivariance

We introduce E(3)-equivariant GNNs to two well-studied fluid-flow systems, namely 3D decaying Taylor-Green vortex and 3D reverse Poiseuille flow. Published at GSI 2023.

We introduce E(3)-equivariant GNNs to two well-studied fluid-flow systems, namely 3D decaying Taylor-Green vortex and 3D reverse Poiseuille flow. Published at GSI 2023.

We introduce a novel method to construct E(n)- and O(n)-equivariant neural networks using Clifford algebras. Published at NeurIPS 2023 (Oral).

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).

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.