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Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
Published in International Conference on Learning Representations (ICLR), 2025
We develop a GNN architecture capable of Long-range propagation exploiting the Hamiltonian formulation of Physical Dynamics
Recommended citation: Heilig S.*, Gravina A.*, Trenta A., Gallicchio C., Bacciu D., (2025). "Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks." International Conference on Learning Representations (ICLR).
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SONAR: Long-Range Graph Propagation through Information Waves
Published in Neural Information Processing Systems (NeurIPS), 2025
We design a novel GNN model capable of long-range propagation using the wave equation on graphs
Recommended citation: Trenta A.*, Gravina A.*, Bacciu D., (2025). "SONAR: Long-Range Graph Propagation through Information Waves" Neural Information Processing Systems (NeurIPS).
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Learning and Transferring Physical Models Trhough Derivatives
Published in Transactions on Machine Learning Research (TMLR), 2026
We introduce DERL, a supervised approach based on partial derivatives that is able to learn physical systems and to transfer physical knowledge across models.
Recommended citation: Trenta A., Cossu A., Bacciu D., (2026). "Learning and Transferring Physical Models through Derivatives" Transactions on Machine Learning Research (TMLR).
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ComPhy: Composing Physical Models with end-to-end Alignment
Published in International Conference on Learning Representations (ICLR), 2026
We introduce ComPhy, a multi-module approach to learn systems of PDEs by assigning one equation to each module. An alignment mechanism ensures the networks share information to solve the system together.
Recommended citation: Trenta A., Cossu A., Bacciu D., (2026). "ComPhy: Composing Physical Models with end-to-end Alignment" International Conference on Learning Representations (ICLR).
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Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics
Published in International Conference on Learning Representations (ICLR), 2026
We propose a novel Graph Neural Simulator that preserves information during propagation, enabling it to model complex physical dynamical systems with long-range dependencies.
Recommended citation: Hoang T., Trenta A.*, Gravina A., Freymuth N., Becker P., Bacciu D., Neumann G., (2026). "Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics" International Conference on Learning Representations (ICLR).
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