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Sparse optimization assisted adaptive and smart hybrid data-driven modeling for process systems
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DOI：10.1016/j.jprocont.2026.103642.png)
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• A hybrid data-driven identification framework integrating partial physical knowledge with sparse regression is proposed. • Convergence analysis is provided, offering theoretical assurance for the proposed approach. • An error-triggered, event-based strategy is employed to handle parameter and structural variations. • Case studies demonstrate the efficacy of the proposed method compared to existing approaches.
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