Deep Learning

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 introduce Geometric Clifford Algebra Networks (GCANs) which parameterize combinations of learnable group actions. Published at ICML 2023.

We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings. Published at ICML 2023 (Spotlight).

We present PDEArena, a modern PyTorch Lightning-based deep learning framework for neural PDE modeling. Published at TMLR 07/2023.

We introduce neural network layers based on operations on composite objects of scalars, vectors, and higher order objects such as bivectors. Published at ICLR 2023.

We present how to use Lie Point Symmetries of PDEs to improve sample complexity of neural PDE solvers. Published at ICML 2022 (Spotlight).

In this work, we introduce a message passing neural PDE solver that replaces all heuristically designed components in numerical PDE solvers with backprop-optimized neural function approximators. Published at ICLR 2022 (Spotlight).

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).