Computational Methods in Systems and Control Theory

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; computational methods 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; data-driven and machine learning methods for system and model identification; computational methods for molecular simulation and electronic structure calculation; high-performance computing with an emphasis on basic linear algebra kernels using hardware accelerators; power-aware (green) computing; scientific machine learning.