The National Science Foundation (NSF) Engineering program (CFDA 47.041) has awarded a $522,014 Project Grant to the Regents of the University of Minnesota to develop data-driven approaches for unraveling the interaction structure of complex dynamical networks. The 3-year award, starting on Aug 1, 2024, aims to devise methods that can correctly identify network links in challenging, real-world scenarios with irregular data, unmeasured components, and temporal patterns. The project will provide results on characterizing power spectral density accuracy, bounding mutual information rate estimation, and recovering low-rank/block-sparse decompositions from temporally correlated data. The goal is to create network reconstruction algorithms with rigorous provable correctness guarantees applicable to diverse systems like neural interactions, weather patterns, and financial markets. No subawards are planned under this grant.
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