| Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics M Lino, S Fotiadis, B Anil A, C Cantwell Physics of Fluids, 2022 | 63* | 2022 |
| Comparing recurrent and convolutional neural networks for predicting wave propagation S Fotiadis, E Pignatelli, M Lino, C Cantwell, A Storkey, AA Bharath ICLR 2020 Workshop on Deep Learning and Differential Equations, 2020 | 42 | 2020 |
| Simulating Surface Wave Dynamics with Convolutional Networks M Lino, C Cantwell, S Fotiadis, E Pignatelli, B Anil A NeurIPS Workshop on Interpretable Inductive Biases and Physically Structured …, 2020 | 20 | 2020 |
| Current and emerging deep-learning methods for the simulation of fluid dynamics M Lino, S Fotiadis, AA Bharath, CD Cantwell Proceedings of the Royal Society A 479 (2275), 20230058, 2023 | 11 | 2023 |
| Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks M Lino, S Fotiadis, B Anil A, C Cantwell ICLR Workshop on AI for Earth and Space Science, 2022 | 10 | 2022 |
| REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics M Lino, S Fotiadis, AA Bharath, CD Cantwell ICLR Workshop on Geometrical and Topological Representation Learning, 2022 | 5 | 2022 |
| Disentangled Generative Models for Robust Prediction of System Dynamics S Fotiadis, M Lino, S Hu, S Garasto, CD Cantwell, AA Bharath ICML 2023, International Conference on Machine Learning, 2023 | 4* | 2023 |
| Data-driven deep-learning methods for the accelerated simulation of Eulerian fluid dynamics (PhD Thesis) M Lino Imperial College London, 2023 | | 2023 |
Anil Anthony BharathDept of Bioengineering, Imperial CollegeVerified email at imperial.ac.uk
