3 research outputs found
Structure Preserving Model Order Reduction by Parameter Optimization
Model order reduction (MOR) methods that are designed to preserve structural
features of a given full order model (FOM) often suffer from a lower accuracy
when compared to their non structure preserving counterparts. In this paper, we
present a framework for MOR based on direct parameter optimization. This means
that the elements of the system matrices are iteratively varied to minimize an
objective functional that measures the difference between the FOM and the
reduced order model (ROM). Structural constraints are encoded in the
parametrization of the ROM. The method only depends on frequency response data
and can thus be applied to a wide range of dynamical systems. We illustrate the
effectiveness of our method on a port-Hamiltonian and on a symmetric second
order system in a comparison with other structure preserving MOR algorithms.Comment: 26 pages, 7 figure