6 citations · 11 across the 5 of their papers we have counts for
5 papers
On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry
Martin Carrasco, Caio F. Deberaldini Netto, Vahan A. Martirosyan +2
Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning. The expressivity of Graph Neural Net…
Augmenting a Physics-Informed Neural Network for the 2D Burgers Equation by Addition of Solution Data Points
Marlon Sproesser Mathias, Wesley Pereira de Almeida, Marcel Rodrigues de Barros +9
We implement a Physics-Informed Neural Network (PINN) for solving the two-dimensional Burgers equations. This type of model can be trained with no previous knowledge of the solutio…
A Physics-Informed Neural Network to Model Port Channels
Marlon S. Mathias, Marcel R. de Barros, Jefferson F. Coelho +8
We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - São…
Enhancing Oceanic Variables Forecast in the Santos Channel by Estimating Model Error with Random Forests
Felipe M. Moreno, Caio F. D. Netto, Marcel R. de Barros +9
In this work we improve forecasting of Sea Surface Height (SSH) and current velocity (speed and direction) in oceanic scenarios. We do so by resorting to Random Forests so as to pr…
Modeling Oceanic Variables with Dynamic Graph Neural Networks
Caio F. D. Netto, Marcel R. de Barros, Jefferson F. Coelho +8
Researchers typically resort to numerical methods to understand and predict ocean dynamics, a key task in mastering environmental phenomena. Such methods may not be suitable in sce…