Research interests
• Data-driven prediction of nonlinear systems in the Koopman framework [2]
• Koopman-based learning and control of non-holonomic systems [3]
• Koopman-based model predictive control [1, 4]
Research projects
• Data-based modeling and predictive control of non-holonomic systems in the context of Koopman theory
(DFG; project number 545246093)
• Active learning within the framework of Koopman operator theory (DFG; project number 535860958)
Short CV
I received my Bachelor’s degree in Mathematics in 2019 and my Master’s degree in Applied Mathematics in 2022, both from the Technische Universität Ilmenau, Germany. In my master thesis, I covered the topic "Discrete velocity models and their relation" to the Navier–Stokes equations”. Now, still in Ilmenau, I pursue a Ph.D. in Mathematics in the Optimization-based Control group supervised by Karl Worthmann. My research focuses on data-based prediction of dynamical systems employing the Koopman operator framework and its application in the context of system theory.
References
[1] Lea Bold, Lars Grüne, Manuel Schaller, and Karl Worthmann. Data-driven mpc with stability guarantees using extended dynamic mode decomposition. IEEE Transactions on Automatic Control, 70(1):534–541, 2025.
[2] Lea Bold, Friedrich M Philipp, Manuel Schaller, and Karl Worthmann. Kernel-based Koopman approximants
for control: Flexible sampling, error analysis, and stability. SIAM Journal on Control and Optimization,
63(6):4044–4071, 2025.
[3] Mario Rosenfelder, Lea Bold, Hannes Eschmann, Peter Eberhard, Karl Worthmann, and Henrik Ebel. Data-
driven predictive control of nonholonomic robots based on a bilinear koopman realization: Data does not replace geometry. Robotics and Autonomous Systems, page 105156, 2025.
[4] Irene Schimperna, Karl Worthmann, Manuel Schaller, Lea Bold, and Lalo Magni. Data-driven model predic-
tive control: Asymptotic stability despite approximation errors exemplified in the koopman framework. arXiv
preprint arXiv:2505.05951, 2025.
