Mario Lino
Mario Lino
Department of Computation Information and Technology, Technical University of Munich (TUM)
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Zitiert von
Zitiert von
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
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
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
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
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
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
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
Data-driven deep-learning methods for the accelerated simulation of Eulerian fluid dynamics (PhD Thesis)
M Lino
Imperial College London, 2023
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