معرفی
Michael Kartmann is a doctoral student and Research Assistant at the Department of Mathematics and Statistics, University of Konstanz, Germany, since October 2022. His work is funded by the BMBF ElAN project, focusing on efficient local waste heat utilization in low-temperature networks, and he collaborates with the YMMOR group (Young Mathematicians in Model Order Reduction).
Research Interests: His work centers on adaptive reduced-order modeling, PDE-constrained optimization, optimal control, and domain decomposition methods for nonlinear preconditioning, with applications in switched low-temperature heat networks and large-scale dynamical systems. He also explores reinforcement learning for optimization problems.
Publications: Recent submissions include methods for model predictive control of switched systems and L1-regularized optimal control. His 2024 published work details adaptive trust region reduced basis approaches for parameter identification, with a 2022 master thesis on hierarchical multiobjective optimization.
Scientific Contributions: He presented talks at conferences including MORE24, IFIP24, and EUCCO23. His software contributions are available on GitHub.
Teaching Roles: He supervises courses such as 'Numerical Mathematics,' 'Proper Orthogonal Decomposition for Linear-Quadratic Optimal Control,' and 'PDE-constrained Optimization' at the University of Konstanz, collaborating with Professors Stefan Volkwein, Behzad Azmi, and others since 2022.
