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Adaptive numerical control for dynamic data-driven applications

Dynamic data-driven applications aim to reconcile different sources of information for systems under scrutiny. Such problems ubiquitously arise in geosciences, for applications like numerical weather prediction, climate change and green energy harvesting. One of the main challenges in solving data-driven applications come from the associated large computational cost. This article presents an adaptive computational framework for fusing numerical model predictions with real observations, in order to generate discrete initial conditions which are optimal in a certain sense. The proposed framework incorporates four-dimensional variational data assimilation, observation impact via sensitivity analysis and adaptive measurement strategies.