M. Meldi

A data assimilation and machine learning approach for the numerical prediction of immersed bodies in oscillating motion

Abstract Data-driven methods have demonstrated strong predictive capabilities in fluid mechanics, yet most current applications still focus on simplified configurations, often characterized by statistical stationarity or limited temporal variability. This work proposes a methodology that combines data assimilation (DA)...