This volume contains selected peer-reviewed contributions from the MoRePaS conference,
Model Reduction of Parametrized Systems, third edition, held at SISSA, International School …

We address the problem of model order reduction (MOR) of parametrized dynamical
systems. Motivated by reduced basis (RB) methods for partial differential equations, we …

Optimal control problems for partial differential equations (PDEs) are often hard to tackle
numerically because their discretization leads to very large scale optimization problems …

Optimal control problems for nonlinear partial differential equations are often hard to tackle
numerically so that the need for developing novel techniques emerges. One such technique …

This book contains selected peer-reviewed contributions submitted by the plenary speakers
to the workshop" Reduced Basis, POD and Reduced Order Methods for model and …

Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

In many fields of science and engineering, like fluid or structural mechanics and electric
circuit design, large–scale dynamical systems need to be simulated, optimized or controlled …

Reduced basis approximation leads to a class of model-reduction methods that can
dramatically decrease the computational cost of solving large, parameterized systems in one …

Model order reduction (MOR) is common in simulation, control and optimization of complex
dynamical systems arising in modeling of physical processes, and in the spatial …

We consider a particular class of structured systems that can be modelled as a set of
input/output subsystems that interconnect to each other, in the sense that outputs of some …