The accurate modeling of dynamical systems often results in a large number of differential equations. In this case, the system matrices then easily become too large to define state-space models (ss objects) in MATLAB. In this contribution we present two new toolboxes that allow the definition and analysis of large-scale models by introducing sparse state-space objects (sss). Through model order reduction (sssMOR) it is possible to obtain high fidelity, low order approximations of the relevant dynamics to further reduce the computational complexity.     «

The accurate modeling of dynamical systems often results in a large number of differential equations. In this case, the system matrices then easily become too large to define state-space models (ss objects) in MATLAB. In this contribution we present two new toolboxes that allow the definition and analysis of large-scale models by introducing sparse state-space objects (sss). Through model order reduction (sssMOR) it is possible to obtain high fidelity, low order approximations of the relevant dy...     »