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Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems
A new physics-informed machine learning framework introduces Fundamental Dynamical Units (FDUs) — signed three-node interaction patterns — to recover causal interaction structure from perturbation time-series data in networked dynamical systems, validated on synthetic benchmarks.
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"Researchers introduced Fundamental Dynamical Units (FDUs) — signed three-node patterns — to infer causal structure from perturbation data in networked systems using physics-informed neural ODEs."
arXiv Machine Learning
Sep 14, 2026