6 papers
Oscillatory Neural Dynamics over Sheaves
Jan-Willem Van Looy, Alessandro Trenta, Alessio Gravina +5
Effective long-range propagation remains a central challenge in graph neural networks, as increasing a model's propagation depth does not guarantee that distant nodes effectively i…
Identifying ODEs from Unstructured Data with Causal Representation Learning
Alessandro Trenta, Riccardo Massidda, Davide Bacciu +1
We study the problem of recovering the governing ODE of a dynamical system from unstructured, high-dimensional observations such as images. Existing methods for ODE discovery typic…
Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics
Tai Hoang, Alessandro Trenta, Alessio Gravina +4
Learning to simulate complex physical systems from data has emerged as a promising way to overcome the limitations of traditional numerical solvers, which often require prohibitive…
Learning and Transferring Physical Models through Derivatives
Alessandro Trenta, Andrea Cossu, Davide Bacciu
We propose Derivative Learning (DERL), a supervised approach that models physical systems by learning their partial derivatives. We also leverage DERL to build physical models incr…
Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
Simon Heilig, Alessio Gravina, Alessandro Trenta +2
The dynamics of information diffusion within graphs is a critical open issue that heavily influences graph representation learning, especially when considering long-range propagati…
MultiSTOP: Solving Functional Equations with Reinforcement Learning
Alessandro Trenta, Davide Bacciu, Andrea Cossu +1
We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on…