arXiv Machine Learning By Riccardo Cappi, Paolo Frazzetto, Nicol\`o Navarin, Alessandro Sperduti

Discovering Generalizable Governing Equations for Graph Dynamical Systems with Interpretable Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2508. 18173v2 Announce Type: replace Abstract: The discovery of symbolic governing equations is a central goal in science; yet, it remains challenging particularly for graph dynamical systems, where the network topology further shapes the system behavior.

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arXiv AI
Jul 16

Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System

arXiv:2607. 13608v1 Announce Type: new Abstract: Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools toward the generation and refinement of mechanistic models of the universe.

By David Krongauz, Arad Zulti, Eran Segal, Teddy Lazebnik