Jia-Jie Zhu, Michael Hintermüller
01.01.2022 − 31.12.2023
A pressing challenge for data-driven control systems is the ubiquitous distribution shift. Building upon our previous theoretical works, this project will develop high-performance learning-based nonlinear ODE and PDE constrained distributionally robust model predictive controllers, highlighting the theory of optimal transport and kernel methods. We expect our methodological innovations to enable new applications of robust machine learning and optimization to areas such as energy markets.