| Abstract: |
| Data assimilation in applications such as numerical weather prediction faces inherent difficulties due to the nonlinear and often chaotic dynamics of the underlying systems. The high dimensionality of these models, coupled with their prohibitive computational cost, forces practitioners to rely on crude approximations. Despite these limitations, data assimilation methods have demonstrated remarkable skill in tracking signals, while rigorous mathematical analysis is still catching up to rigorously proof corresponding accuracy results. A central challenge going forward is to develop more accurate representations of uncertainty both for reliable estimation and for solving optimal design problems related to observation strategies and filtering. In this talk, we will explore these challenges and present state-of-the-art approaches that aim to bridge the gap between practice and theory. |
|