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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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