Analysis of effects due to parasitics is of vital importance during the design of large-scale integrated circuits, since it gives insight into how circuit performance is affected by undesired parasitic effects. Due to the increasing amount of interconnect and metal layers, parasitic extraction and simulation may become very time consuming or even unfeasible. Developments are presented, for redu...

We present recent advances in the mixed symbolic and numeric model reduction techniques for automatically extracting the dominant system behavior of multiphysical systems. A unique feature of our approach is to compute approximated symbolic formulas for linear and nonlinear system characteristics, rigorously reducing the complexity of symbolic expressions while controlling a user-given error bo...

In this survey, we provide an overview of model order reduction (MOR) methods applied to coupled systems. The coupling can be linear or nonlinear, weak or strong. Physically, the coupling may come from structurestructure coupling, fluid-structure or fluid-fluid interaction, electro-thermal coupling, electroor thermal-mechanical coupling, or circuit-device coupling. Different MOR methods for cou...

Partial order reduction helps improve the performance of a (sequential) model-checker by eliminating the interleaving of independent actions. In this paper, we show how to combine partial order reduction and parallel distributed model-checking. We point out that an appropriate partial order reduction algorithm is to be chosen to avoid sequentializing an otherwise parallelizable activity. We pro...

Large scale wave propagation simulation is currently achievable in reasonable turnaround times by using distributed computing in multiple cpu clusters. However, if one needs to perform many such simulations, as is the case in optimization, tomography, or seismic imaging, then the resources required are still prohibitive. Model order reduction of large dynamical systems has been successfully use...

Modeling of real time system posses a large number of problems. It is a challenging task to model accurately a large real time system. Modeling of large real time systems results in large number of differential or difference equations that lead to state variable or transfer function models that represents a higher order system. It is very difficult to handle such a higher order system model for...

This Chapter contains three advanced topics in model order reduction (MOR): nonlinear MOR, MOR for multi-terminals (or multi-ports) and finally an application in deriving a nonlinear macromodel covering phase shift when coupling oscillators. The Sections are offered in a prefered order for reading, but can be read independentlty. Section 5.1, written by Michael Striebel and E. Jan W. ter Maten,...

Feedback control of fluids is often achieved with limited state measurements. Effective approaches utilize compensators, which can be computed using linear quadratic Gaussian (LQG) or MinMax control designs. However, to achieve real-time control algorithms, these compensators must have low computational complexity. Model reduction methods are a natural choice to build reduced-order compensators...

The simulation of electric rotating machines is both computationally expensive and memory intensive. To overcome these costs, model order reduction techniques can be applied. The focus of this contribution is especially on machines that contain non-symmetric components. These are usually introduced during the mass production process and are modeled by small perturbations in the geometry (e.g., ...

Model checking using GPUs has seen increased popularity over the last years. Because GPUs have a limited amount of memory, only small to medium-sized systems can be verified. For on-the-fly explicitstate model checking, we improve memory efficiency by applying partialorder reduction. We propose novel parallel algorithms for three practical approaches to partial-order reduction. Correctness of t...