arXiv Machine Learning June 8, 2026 By Moyi Tian, Daniel A. Messenger, Vanja Dukic, Nancy Rodr\'iguez, David M. Bortz

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy

Read the original on arXiv Machine Learning →

arXiv:2605. 30432v2 Announce Type: replace-cross Abstract: Social systems consist of networks of individuals who influence one another through social interactions.

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Hugging Face Trending Papers

Jun 10

How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering. While early approaches relied on empirical data and heuristics, modern data-driven methods offer greater flexibility and fewer assumptions.

arXiv Machine Learning

Jun 11

How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

arXiv:2606. 12182v1 Announce Type: new Abstract: Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering.

By Ana Larra\~naga, Urban Fasel, Steven L. Brunton

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

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Tensor-Train Weak SINDy: Identifying High-Dimensional Nonlinear Dynamics

arXiv:2609.09434v1 Announce Type: cross Abstract: In recent years, weak-form methods have made significant advances in data-driven discovery of dynamical systems. However, in high-dimensional setting...

By Will Houser, Vanja Dukic, David M. Bortz

arXiv Machine Learning

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Learning Interaction Kernels from Collective Steady States

The paper introduces a learning method for identifying interaction kernels in particle systems using only single-snapshot observations of collective steady states, rather than trajectory data. By regularizing with empirical distributions from varied, unseen initial conditions, the authors address the ill‑posed inverse problem and demonstrate stable, accurate recovery of interaction laws across several models. The recovered laws enable faithful reproduction of both steady‑state patterns and, in many cases, the preceding dynamics.

By Baoli Hao, Mauro Maggioni, Ming Zhong

arXiv Machine Learning

Jun 8

Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach

arXiv:2606. 07483v1 Announce Type: new Abstract: Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion.

By Lei Huang

diffusion safety

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