6 citations · 6 across the 2 of their papers we have counts for
3 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…