About

The 2018 Spring School features six lecturers who are considered high-caliber representatives of their respective area of expertise. The lectures are tutorial in nature and are interlaced with panel discussions and small group discussions allowing participants to actively engage.

The content being covered is organized under our main overarching theme, namely to foster synergetic syntheses of, on the one hand, classical “model based” and, on the other hand, “data-driven” methodologies such as forward and inverse tasks in Uncertainty Quantification, parameter and state estimation, data assimilation, machine learning, structural imaging in material science, and modeling.

We plan to pair these topics with recent methodological developments, in particular those that are able to cope with the challenge of spatial high-dimensionality shared by all the above topics. Examples, to name a few, are sparse high-dimensional polynomial expansions, low-rank and tensor methods, certifiable model order reduction concepts, and sparsity promoting regularization concepts and greedy strategies.

Schedule