| GRAND: Graph Neural Diffusion BP Chamberlain, J Rowbottom, M Gorinova, S Webb, E Rossi, ... ICML, 2021 | 454 | 2021 |
| Graph-Coupled Oscillator Networks TK Rusch, BP Chamberlain, J Rowbottom, S Mishra, MM Bronstein arXiv preprint arXiv:2202.02296, 2022 | 208 | 2022 |
| Beltrami Flow and Neural Diffusion on Graphs BP Chamberlain, J Rowbottom, D Eynard, F Di Giovanni, X Dong, ... NeurIPS, 2021 | 121 | 2021 |
| Understanding convolution on graphs via energies F Di Giovanni, J Rowbottom, BP Chamberlain, T Markovich, MM Bronstein arXiv preprint arXiv:2206.10991, 2022 | 74 | 2022 |
| Graph neural networks as gradient flows F Di Giovanni, J Rowbottom, BP Chamberlain, T Markovich, MM Bronstein arXiv preprint arXiv:2206.10991, 2022 | 70 | 2022 |
| Equivariant Mesh Attention Networks S Basu, J Gallego-Posada, F Viganò, J Rowbottom, T Cohen arXiv preprint arXiv:2205.10662, 2022 | 16 | 2022 |
| Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups Z Shumaylov, P Zaika, J Rowbottom, F Sherry, M Weber, CB Schönlieb arXiv preprint arXiv:2410.02698, 2024 | 12 | 2024 |
| G-Adaptivity: optimised graph-based mesh relocation for finite element methods J Rowbottom, G Maierhofer, T Deveney, E Müller, A Paganini, K Schratz, ... 42nd International Conference on Machine Learning, 2025 | 5 | 2025 |
| Multi-Level Monte Carlo Training of Neural Operators J Rowbottom, S Fresca, P Lio, CB Schönlieb, N Boullé arXiv preprint arXiv:2505.12940, 2025 | 3 | 2025 |
| G-Adaptive mesh refinement--leveraging graph neural networks and differentiable finite element solvers J Rowbottom, G Maierhofer, T Deveney, K Schratz, P Liò, CB Schönlieb, ... arXiv preprint arXiv:2407.04516, 2024 | 3 | 2024 |
| Graph-Coupled Oscillator Networks T Konstantin Rusch, BP Chamberlain, J Rowbottom, S Mishra, ... arXiv e-prints, arXiv: 2202.02296, 2022 | | 2022 |
| GRAND: Graph Neural Diffusion Supplementary Material BP Chamberlain, J Rowbottom, M Gorinova, S Webb, E Rossi, ... | | |
