AN ENTROPIC FRAMEWORK FOR SPARSE NETWORK RECONSTRUCTION IN NONLINEAR DYNAMICAL SYSTEMS Emanuele Bossi , Data Science and Software Engineering
MENTOR Abd AlRahman Rasheed AlMomani, Mathematics
Synchronization phenomena in networks of interacting dynamical systems arise in a wide range of scientific and engineering domains, including neuroscience, power systems, and multi-agent coordination. This project introduces an entropic framework for the reconstruction of sparse interaction networks governed by Kuramoto-type oscillator dynamics. The central objective of the work is to address the inverse problem of network reconstruction: inferring unknown coupling structures from observed time-series data of oscillator phases. The reconstruction task is formulated as a nonlinear system identification problem that is linear in the unknown interaction weights. To solve this problem, an Entropic Regression framework is implemented, combining information- theoretic feature selection with projection-based estimation to identify sparse interaction structures and estimate coupling strengths. The resulting framework provides a principled approach for inferring connectivity in complex dynamical systems and establishes a foundation for data-driven analysis of synchronization phenomena in settings where network structure is not directly observable.
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18 | URI DISCOVERY 2026
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