# Model Order Reduction: Techniques and Tools

@article{Benner2015ModelOR, title={Model Order Reduction: Techniques and Tools}, author={Peter Benner and Heike Fa{\ss}bender}, journal={Encyclopedia of Systems and Control}, year={2015} }

Model order reduction (MOR) is here understood as a computational technique to reduce the order of a dynamical system described by a set of ordinary or differential-algebraic equations (ODEs or DAEs) to facilitate or enable its simulation, the design of a controller, or optimization and design of the physical system modeled. It focuses on representing the map from inputs into the system to its outputs, while its dynamics are treated as a blackbox so that the large-scale set of describing ODEs…

## 6 Citations

### Robust Output Tracking for a Room Temperature Model with Distributed Control and Observation

- Mathematics
- 2020

### Reduced Order Controller Design for Robust Output Regulation

- MathematicsIEEE Transactions on Automatic Control
- 2020

It is shown that robust output tracking and disturbance rejection for this class of systems can be achieved using a finite-dimensional controller and algorithms for construction of two different internal model based robust controllers are presented.

### A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes

- Computer ScienceInternational Journal for Uncertainty Quantification
- 2021

The comparison shows that the proposed method improves the active subspace recovery and predictive accuracy, in both the deterministic and probabilistic sense, when only few model observations are available for training, at the cost of increased training time.

### A brief note on the generalized singular perturbation approximation

- Mathematics2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)
- 2015

The generalized singular perturbation approximation is a method for approximation of dynamical systems which has been presented in the literature on model reduction over the past two decades. In this…

### Clustering as Approximation Method to Optimize Hydrological Simulations

- Computer ScienceEuro-Par
- 2019

This paper extends this approach by applying machine learning methods to cluster functionally similar model units and by running the model only on a small yet representative subset of each cluster, which achieves the best trade-off between decreasing computation time and increasing simulation uncertainty.

### On Using Clustering for the Optimization of Hydrological Simulations

- Computer Science2018 IEEE International Conference on Data Mining Workshops (ICDMW)
- 2018

This work shows an ongoing project that applies existing clustering methods to identify functionally similar model units and runs the model only on representative model units, to reduce model redundancies and computation complexities.

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The aim of this book is to survey some of the most successful model reduction methods in tutorial style articles and to present benchmark problems from several application areas for testing and comparing existing and new algorithms.

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