Optimal control of complex dynamical systems can be challenging due to cost constraints and analytical intractability. In the work "AI Pontryagin or how neural networks learn to control dynamical systems", we propose a machine-learning-based control framework able to learn quasi-optimal control signals and steer complex high-dimensional dynamical systems towards a desired target state.

This new theme issue with contributions from HMI members focuses on recent developments in data-science approaches for epidemic management, with a particular focus on combating the ongoing COVID-19 pandemic.

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