activity
20192022
most citedSimulating Surface Wave Dynamics with Convolutional Networks

9 citations · 27 across the 4 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn20229 cited

Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks

Mario Lino, Stathi Fotiadis, Anil A. Bharath +1

Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have…

cs.LG20213 cited

Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks

Mario Lino, Chris Cantwell, Anil A. Bharath +1

Continuum mechanics simulators, numerically solving one or more partial differential equations, are essential tools in many areas of science and engineering, but their performance…

cs.LG20209 cited

Simulating Surface Wave Dynamics with Convolutional Networks

Mario Lino, Chris Cantwell, Stathi Fotiadis +2

We investigate the performance of fully convolutional networks to simulate the motion and interaction of surface waves in open and closed complex geometries. We focus on a U-Net ar…

cs.LG2020

Comparing recurrent and convolutional neural networks for predicting wave propagation

Stathi Fotiadis, Eduardo Pignatelli, Mario Lino Valencia +3

Dynamical systems can be modelled by partial differential equations and numerical computations are used everywhere in science and engineering. In this work, we investigate the perf…

stat.ML20196 cited

An Empirical Evaluation of Adversarial Robustness under Transfer Learning

Todor Davchev, Timos Korres, Stathi Fotiadis +2

In this work, we evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we st…