arXiv Machine Learning By Janine Strotherm, Luca Hermes, Andr\'e Artelt, Barbara Hammer

MeGA-MP: Metric Graph Advection Message Passing -- A Physics-Informed Message Passing Operator for Advection-Dominated Metric Graphs

Read the original on arXiv Machine Learning →

arXiv:2607. 05167v1 Announce Type: new Abstract: Many real-world systems are organized as networks where spatio-temporal dynamics unfold along connections and not discretely between nodes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 20

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

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.

By Riccardo Cappi, Paolo Frazzetto, Nicol\`o Navarin, Alessandro Sperduti
arXiv Machine Learning
Jun 18

INDEQS: Informed Neural controlled Differential EQuationS

arXiv:2606. 19138v1 Announce Type: new Abstract: Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori.

By Michael Detzel, Gabriel Nobis, Kristiyan Blagov, Juri Schubert, Jackie Ma, Wojciech Samek
arXiv Machine Learning
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Topology enables learning-based hydrodynamic prediction of the global river system

arXiv:2602.22293v2 Announce Type: replace Abstract: Accurate river prediction is essential for water, food and energy security, yet remains challenging across entire river networks. Machine learning...

By Hancheng Ren, Gang Zhao, Shuo Wang, Louise Slater, Dai Yamazaki, Shu Liu, Jingfang Fan, Xueying Li, Shibo Cui, Ziming Yu, Shengyu Kang, Depeng Zuo, Dingzhi Peng, Zongxue Xu, Bo Pang