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The purpose of MOR Wiki is to bring together experts in the area of model order reduction, as well as researchers from application areas in an attempt to provide a platform for exchanging ideas.

Modeling and numerical simulation are unavoidable in many application research areas, e.g. reaction process, micro-electro-mechanical systems (MEMS) design, control design, etc.. The processes or devices can be modeled by partial differential equations (PDEs). To simulate such models, spatial discretization via e.g. finite element discretization is necessary, which results in a system of ordinary differential equations (ODEs), or differential algebra equations (DAEs).

After spatial discretization, the number of degrees of freedom is usually very high. It is therefore very time consuming to simulate the large-scale systems of ODEs or DAEs. Developed from well-established mathematical theories and robust numerical algorithms, model order reduction (MOR) has been recognized as very efficient for reducing the simulation time of large-scale systems. Through model order reduction, a small system with reduced number of equations (reduced model) are derived. The reduced model is simulated instead, and the solution of the original PDEs or ODEs can then be recovered from the solution of the reduced model. As a result, the simulation time of the original large-scale system can be shortened by several orders of magnitude. The reduced model as a whole can also replace the original system and be reused repeatedly during the design process, which can further save much time.

Parametric model order reduction (PMOR) methods are designed for model order reduction of parametrized systems, where the parameters of the system play an important role in practical applications such as Integrated Circuit (IC) design, MEMS design, Chemical engineering etc.. The parameters could be the variables describing geometrical measurements, material properties, the damping of the system or the component flow-rate. The reduced models are constructed such that all the parameters can be preserved with acceptable accuracy.

In MOR Wiki, one can find some special pages which provide benchmarks of parametric models, and some pages which explain applicable PMOR methods. Following the submission rules, one can also submit new bechnmarks of parametric models.

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