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cs.LG2026
Efficient Adaptation of ROMs for Unsteady Flows Using Data Assimilation
Ismaël Zighed, Andrea Nóvoa, Luca Magri +1
We propose an efficient retraining strategy for a parameterized Reduced Order Model (ROM) that attains accuracy comparable to full retraining while requiring only a fraction of the…
cs.LG2025
UP-dROM : Uncertainty-Aware and Parametrised dynamic Reduced-Order Model, application to unsteady flows
Ismaël Zighed, Nicolas Thome, Patrick Gallinari +1
Reduced order models (ROMs) play a critical role in fluid mechanics by providing low-cost predictions, making them an attractive tool for engineering applications. However, for ROM…