Rigorous results in parameter reconstruction in fluid systems

The need to identify latent components in predictive models is crucial for accurate forecasting. In the context of fluids, these latent components may arise as unknown diffusivities, viscosities, or source terms. This talk will present data assimilation algorithms for systematically reconstructing these parameters from time-series observations in an idealized scenario and discuss their convergence analysis.

Bio
https://math.hunter.cuny.edu/vmartine/