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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

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Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Headings are cool

You can have many headings

Aren’t headings cool?

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Headings are cool

You can have many headings

Aren’t headings cool?

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Headings are cool

You can have many headings

Aren’t headings cool?

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Headings are cool

You can have many headings

Aren’t headings cool?

portfolio

publications

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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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).
Download Paper | Download Slides | Download Bibtex

talks

teaching