In the last five years, the field of nonlinear control has seen some drastic new developments. New theoretical advances are reshaping how we analyze and design complex dynamical systems. In particular, contraction theory has emerged as a powerful framework for studying stability, robustness, and performance in deterministic and stochastic nonlinear control systems. By characterizing the differential behaviour of trajectories, contraction-based methods provide powerful guarantees that extend far beyond traditional Lyapunov analysis. At the same time, learning-based control is rapidly evolving, offering data-driven representations of system dynamics and controllers. The interplay between contraction theory and learning is giving rise to provably stable learning architectures. These developments mark a shift toward scalable and certifiable nonlinear control methods, with direct relevance to modern engineering systems ranging from robotics and energy to aerospace. This winter school will explore recent advances in contraction theory and learning-based control for nonlinear systems. Participants will gain insight into the latest theoretical tools and their applications to real-world systems. The program features lectures by rising stars in the field, complemented by challenge-based problems to be solved in teams. On the final day, each team will present their results in an interactive session.

The course syllabus, including the schedule, can be downloaded below.

The lecture materials and exercises will be made available in due time.

Amritam Das (TU Eindhoven)
Sebastiaan van den Eijnden (TU Eindhoven)

Maaike Kraeger-Holland (Office manager DISC)
Bayu Jayawardhana (Director of DISC)