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Anne Koch
Researcher at University of Stuttgart
Publications - 27
Citations - 577
Anne Koch is an academic researcher from University of Stuttgart. The author has contributed to research in topics: Computer science & Robustness (computer science). The author has an hindex of 8, co-authored 27 publications receiving 292 citations. Previous affiliations of Anne Koch include Royal Institute of Technology & Technion – Israel Institute of Technology.
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Journal ArticleDOI
Robust and optimal predictive control of the COVID-19 outbreak.
TL;DR: In this article, the authors investigate adaptive strategies to robustly and optimally control the COVID-19 pandemic via social distancing measures based on the example of Germany and propose a robust MPC-based feedback policy using interval arithmetic.
Proceedings ArticleDOI
Robust data-driven state-feedback design
TL;DR: This work considers the problem of designing robust state-feedback controllers for discrete-time linear time-invariant systems, based directly on measured data, and shows how the proposed framework can be extended to take partial model knowledge into account.
Journal ArticleDOI
Robust and optimal predictive control of the COVID-19 outbreak
TL;DR: The theoretical findings support various recent studies by showing that adaptive feedback strategies are required to reliably contain the COVID-19 outbreak, and well-designed policies can significantly reduce the number of fatalities compared to simpler policies while keeping the amount of social distancing measures on the same level.
Journal ArticleDOI
Training Robust Neural Networks Using Lipschitz Bounds
TL;DR: An optimization scheme based on the Alternating Direction Method of Multipliers that minimizes not only the training loss of an NN but also its Lipschitz constant resulting in a semidefinite programming based training procedure that promotes robustness.
Posted Content
Provably robust verification of dissipativity properties from data
TL;DR: This paper presents a framework for verifying dissipativity properties from measured data with desirable guarantees in the case of input-state measurements, and extends this approach to input-output data, where similar results hold in the noise-free case.