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Data-driven model order reduction via T-SVD | Synapse
March 3, 2026
Data-driven model order reduction via T-SVD
SM
Shenghan Mei
FL
F. Liu
University of North Carolina at Chapel Hill
YM
Yidan Mei
University of North Carolina at Chapel Hill
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Puntos clave
Model order reduction techniques can significantly enhance computational efficiency and performance in simulations.
The T-SVD approach utilizes singular value decomposition for effective data-driven analysis in system dynamics.
Utilizing a data-driven framework may enable improved modeling in complex systems for better decision-making.
While promising, the implications of T-SVD techniques require further exploration in various application contexts.
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Mei et al. (Fri,) studied this question.
synapsesocial.com/papers/69a76882badf0bb9e87e4e91
https://doi.org/https://doi.org/10.1016/j.automatica.2026.112862