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Data Assimilation in Model Order Reduction Techniques for Computational Mechanics

Nissrine AkkariSafran Group

Fabien Casenave, Safran Group

David Ryckelynck, MINES ParisTech

The aim of this mini-symposium is to meet the two following research domains  “Data science” and “Model Order Reduction” in the Computational Mechanics field, to provide the optimal estimate of the evolving state of a mechanical system. Data science is an important tool for the classification and regression of the large amount of scientific data coming from the High Fidelity simulations, and Model Order Reduction techniques provide an efficient physical tool to forecast some new states of the mechanical system. Today numerical models can assimilate massive data from experiment and/or from numerical predictions.