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Graph dynamical systems are hard to model because their behavior is shaped by network topology. A new approach uses interpretable neural networks to discover governing equations. This could help us understand complex systems, but it's still unclear how well it works in practice.
“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 …”
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arXiv:2508.18173v2, Interpretable Neural Networks
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Aug 6, 2026