Computational Methods in Systems and Control Theory

Major research interests

• Scientific machine learning: physics-informed (A/D/C)NNs, data-enhanced computational sciences
• Simulation, control and optimization of dynamical systems, described by systems of ordinary, differential, and partial differential equations
• Model order reduction methods for dynamical and parametric systems, including methods based on system balancing, rational interpolation, proper orthogonal decomposition and reduced basis methods; data-driven methods for model identification and 3D imaging
• Computational methods in for systems and control, in particular system norms, Riccati-based control design, stabilization of nonlinear systems
• Numerical linear algebra with an emphasis on exploiting low-rank structures in solving linear systems with tensor structure as well as on linear and nonlinear eigenvalue problems
• Computational methods for molecular simulation and electronic structure calculation
• High-performance computing with an emphasis on basic linear algebra kernels using hardware accelerators and power-aware (green) computing

Detailed information on the group's research topics can be found here.

Current PhD students in the IMPRS program

Shuwen Sun

Website

PhD project:"Non-intrusive model order reduction of nonlinear dynamical systems"

IMPRS Alumni