Neural Surrogates

Clifford Neural Layers for PDE Modeling

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.

Lie Point Symmetry Data Augmentation for Neural PDE Solvers

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

Message Passing Neural PDE Solvers

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).

Boundary Graph Neural Networks for 3D Simulations

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