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leading aerospace organizations. It is a subproject of a NATO-wide initiative where participating organizations can test new control algorithms on sub-scale 3D-printed aircraft, developed and provided by
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uncertainties. Determining how to account for these uncertainties leads to research questions spanning from data collection and estimation to model representations and optimization algorithms. In the field
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning - constitute fundamental scientific domains that act as
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models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view of machine learning which clearly integrates the two subject areas within the division